77

REFERENCES

[1] J. G. Proakis, Digital Communications. New York: Mc-Graw Hill Inc., 1995.

[2] B. R. Peterson, D. D. Falconer, Minimum Mean Square Equalization in Cyclostationary and

Stationary Interference-Analysis and Subscriber Line Calculations, IEEE Journal on Selected

Areas in Communication, 9 (1991): 931–940.

[3] J. H. Winter, Optimum Combining in Digital Mobile Radio with Co-channel Interference,

IEEE Journal on Selected Areas in Communication, 2 (1984): 528–539.

[4] J. G. Proakis, Adaptive Equalization for TDMA Digital Mobile Radio, IEEE Trans. on

Vehicular Technology, 40 (1991): 333–341.

[5] K. Feher, MODEMS for Emerging Digital Cellular-Mobile Radio system, IEEE Trans. on

Vehicular Technology, 40 (1991): 355–365.

[6] B. Widrow, M. E. Hoff(Jr), Adaptive Switching Circuits, in IRE WESCON Conv., vol. 4,

(1960): 94–104.

[7] R. W. Lucky, Automatic Equalization of Digital Communication, Bell System Tech. J, 44

(1965): 547–588.

[8] G. D. Forney, Maximum-Likelihood Sequence Estimation of Digital Sequences in the

Presence of Inter-symbol Interference, IEEE Transactions on Information Theory, 18 (1972):

363–378.

[9] G. D. Forney, The Viterbi Algorithm, Proceedings of the IEEE, 61 (1973): 268–278.

[10] D. A. George, R. R. Bowen, J. R. Storey, An Adaptive Decision Feedback Equaliser’,

IEEE Transactions on Communication Technology, 19 (1971): 281–293.

[11] D. D. Falconer, Joint Adaptive Equalization and Carrier Recovery in Two-dimensional

Digital Communication Systems, Bell System Technical Journal, 55 (1976): 317–334.

78

[12] D. Godard, Channel Equalization Using Kalman Filter for Fast Data Transmission, IBM

Journal Res. Development, 18 (1974): 267–273.

[13] J. Makhoul, A Class of All-Zero Lattice Digital Filters, IEEE Transactions on Acoustics,

Speech and Signal Processing, 26 (1978): 304–314.

[14] J. R. Treichler, I. Fijakow, C. R. Johnson, Fractionally Spaced Equalizers – How Long

Should They Realy be?, IEEE Signal Processing Magazine, (1996): 65–81.

[15] S. U. H. Qureshi, Adaptive Equalization, Proceedings of the IEEE, 73 (1985): 1349–

1387.

[16] S. Haykin, Neural Networks - A Comprehensive Foundation. New York: Macmillan,

1994.

[17] R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis. John Wiley and Sons,

1973.

[18] S. Chen, B. Mulgrew, S. McLaughlin, Adaptive Bayesian Equalizer with Decision

Feedback, IEEE Transactions on Signal Processing, 41 (1993): 2918–2927.

[19] J. F. Hayes, T. M. Cover, J. B. Riera, Optimal Sequence Detection and Optimal Symbol-

by-Symbol Detection: Similar Algorithms, IEEE Transactions on Communications, 30

(1982): 152–157.

[20] S. Haykin, Adaptive Filter Theory. Englewood Cliff, NJ, USA: Prentice Hall, 1991.

[21] B. Mulgrew, P. M. Grant, J. S. Thompson, Digital Signal Processing: Concepts and

Applications. Houndmills, Basingstoke, U.K.: Macmillan, 1 ed., 1999.

[22] B. Mulgrew, Nonlinear Signal Processing for Adaptive Equalisation and Multi-user

Detection, in Proceedings of the European Signal Processing Conference, EUSIPCO, (Island

of Rhodes, GREECE), 8-11 September 1998): 537–544.

79

[23] J. Cid-Sueiro, A. R. Figueiras-Vidal, Channel Equalization with Neural Networks, in

Digital Signal Processing in Telecommuniocations - European Project COST#229 Technical

Contributions (A. R. Figueiras-Vidal, ed.), London, U.K.: Springer-Verlag, 1996, pp. 257–

312.

[24] B. Mulgrew, Applying Radial Basis Functions, IEEE Signal Processing Magazine, 13

(1996): 50–65.

[25] Roger Wood, Enhanced Decision Feedback Equalization, IEEE Transactions on

Magnetics, 26 (5) (1990): 2178 - 2180

[26] S. M. Elnoubi, H. Badr, E. A. Youssef, BER Improvement of PRCPM in Mobile Radio

Channels with Discriminator Detection Using DFE, IEEE Transactions on Vehicular

Technology, 40 (4) (1991): 694 - 699

[27] O. Andrisano, M. Chiani, R. Verdone, Performance of Narrowband CPM Systems with

Limiter-Discriminator-Integrator Detection and Decision Feedback Equalization in Mobile

Radio Channels, IEEE Transactions on Vehicular Technology, 42 (2): (1993): 166 - 176

[28] J. G. Kenney, A System Architecture for Multi-level Decision Feedback Equalization,

IEEE Transactions on Magnetics, 30 (6) (1994): 4218 - 4220

[29] J. G. Kenney, R. Wood, Multi-level Decision Feedback Equalization: An Efficient

Realization of FDTS/DF, IEEE Transactions on Magnetics, 31 (2) (1995): 1115 - 1120

[30] P. A. McEwen, J. G. Kenney, All pass Forward Equalizer for Decision Feedback

Equalization, IEEE Transactions on Magnetics, 31 (6) (1995): 3045,3047

[31] H. Witschnig, K. Reich, H. Stallinger, R. Weige, A. Springer, Performance versus Effort

for Decision Feedback Equalization - An Analysis based on SC/FDE adapted to IEEE

802.11a’, IEEE International conference on Communication, (2004): 3455- 3459.

80

[32] A. Koppler, A. Springer and R. Weigel, Combined Frequency Domain Feedforward and

Turbo DFE for Single Carrier W–LAN Systems, IEEE International Conference on

Communications, (2003): 2119 – 2123.

[33] A. Koppler, M. Hueme, A. Springer, R. Weige1, Iterative Combined DFE and Decoding

for Broadband Wireless Single Carrier Systems, 10th International Conference on

Telecommunications, (2003): 743 – 747.

[34] M. A. Aldajani, Power–of-two quantization in decision feedback equalization, IEEE

International Conference on Acoustics, Speech and Signal Processing, (2006): IV – IV.

[35] M. B. Shenouda, T N. Davidson, A Framework for designing MIMO systems with

decision feedback equalization or Tomlinson-Harashima precoding, IEEE Journal on

Selected Areas in Communications, 26 (2) (2008): 401 – 411.

[36] D.W. Bartel, L.B. White, Decision-feedback equalization with fixed-lag smoothing in

nonlinear channels, Digital Signal Processing 16 (2006): 577–587

[37] Y. Jin, F. Zhang. W. Wu, Reduced-Complexity Turbo Equalization for Turbo Coded

MIMO/OFDM Systems, The Journal of China Universities of posts & telecommunications,

13 (1) (2006): 93–98

[38] A. Elkhazin, K. N. Plataniotis, S. Pasupathy, BER analysis of Bayesian equalization

using orthogonal hyper-planes, Signal Processing 86 (2006): 1992–2000

[39] M. A. Vazquez, M. F. Bugall, J. Miguez, Sequential Monte Carlo methods for

complexity-constrained MAP equalization of dispersive MIMO channels, Signal Processing

88 (2008): 1017–1034

81

[40] S. Chen, S. Liu, L. Hanzo, Adaptive Bayesian space–time equalization for multiple

receive-antenna assisted single-input multiple-output systems, Digital Signal Process. 18

(2008): 622-634

[41] A.K. Pradhan, S.K. Meher, A. Routray, Communication channel equalization using

wavelet network, Digital Signal Processing 16 (2006): 445–452.

[42] Y. Ye, S.S.Abeysekera, Efficient blind estimation and equalization of non-minimum

phase communication channels via the use of a zero forcing equalizer, Signal Processing 86

(2006): 1019–1034.

[43] A. T. Erdogan, On the convergence of subgradient based blind equalization algorithm

Signal Processing 86 (2006): 3732–3738

[44] S. A. Sheikh, P. Fan, Two efficient adaptively varying modulus blind equalizers: AVMA

and DM/AVMA, Digital Signal Processing 16 (2006): 832–845

[45] K. Banovic, E. Abdel-Raheem, M. A.S. Khalid, Computationally efficient methods for

blind decision feedback equalization of QAM signals, International Journal of Electronics

and Communication (AEU) 62 (5) (2008): 374–385

[46] R. Pan, C.L Nikias, The complex cepstrum of higher order cumulants and nonminimum

phase identification, IEEE Transactions on Acoustic Speech Signal Process. 36 (1988): 186-

205.

[47] D. Hatzinakos, Blind equalization based on polyspectra, Ph.D. dissertation, Northeastern

Univ., Boston, MA, 1990.

[48] D. Hatzinakos, C.L. Nikias, Blind equalization based on higher-order stastistics (H.O.S)’,

in Blind Deconvolution, S. Haykin, Ed. Englewood Cliffs, NJ: Prentice-Hall, 1994.

82

[49] J.A. Cadzow, Blind deconvolution via cumulant extrema, IEEE Signal Procssing

Magagine, 13 (1996): 24-42.

[50] C.-Y. Chi, M.-C. Wu, Inverse filter criteria for blind deconvolution and equalization

using two cumulants, IEEE Transactions on Signal Processing, 43 (1) (1995): 55-63.

[51] J.K Tugnait, Estimation of linear parametric models using inverse filter criteria and HOS,

IEEE Transactions on Signal Processing , 41 (1993):.3196-3199.

[52] M.C Campi, R. Leonardi, L.A Rossi, Generalized super-exponential method for blind

equalization using Kautz filters, in Proceedings of 1999 IEEE Signal Processing Workshop

Higher-Order Stastistics, Caesares, Israel, (June 1999): 107-111.

[53] F. Herrmann, A.K Nandi, Reduced computation blind super-exponential equalizer,

Electronics Letters, 34 (23) (1998): 2208-2210.

[54] F.Hermann, A.K Nandi, Super-exponential equalizer- a modified eigenvector algorithm

(MEVA)’, in Proceedings of 1999 IEEE Signal Processing Workshop Higher-Order

Statistics, Caesarea, Israel,, (June 1999): 93-96.

[55] G.Scarano, G. Jacovitti, On the optimality of Bussgang and super exponential blind

deconvolution methods, in Proceedings of 1997 IEEE Signal Processing Workshop Higher-

Order Statistics, Banff, Camada, (July 1997): 244-247.

[56] D. Wu, X. Gu, Q. Guo, Improved super-exponential algorithm for blind equalization,

Digital Signal Processing, 19 (3) (2007): 444-451.

[57] A.G Bessios, C.L. Nikias, POTEA: The power cepstrum and tricoherence equalization

algorithm, IEEE Transactions on Communications, 43 (1995): 2667-2671.

83

[58] D.H Brooks, C.L Nikias, Cross-bicepstrum and cross-tricepstrum approaches to

multichannel deconvolution, in Proceedings of International Signal Processing Workshop

higher-Order Statistics, Chamrousse, France, (July 1991): 91-94.

[59] Y. Sato, A method of self-recovering equalization for multilevel amplitude modulation’,

IEEE Transactions on Communications, 23 (1975): 679-682.

[60] D.N. Godard, Self-recovering equalization and carrier tracking in two dimensional data

communication systems, IEEE Transactions on Communications, 28 (1980): 1867-1875,.

[61] C.R. Johnson P. Schmiter, T.J. Endres, J.D. behm, D.r. Brown, R.A. Casas, Blind

equalization using the constant modulus criterion: A review, Proceedings of IEEE, 86 (1998):

1927-1950.

[62] L.R Litwin, Blind channel equalization, IEEE Potentials, 18 (1999): 9-12.

[63] J.R Treichler, M.G. Larimore, New processing techniques based on the constant modulus

adaptive algorithm, IEEE Transactions on Acoustics, Speech, Signal Processing, 33 (1985):

420-431.

[64] J.R Treichler, B.G Agee, A new approach to multipath correction of constant modulus

signals, IEEE Transactions on Acoustics, Speech, Signal Processing, 31 (1983): 349-472.

[65] A. Benveniste, M.Goursat, Blind Equalizers, IEEE Transactions on Communications, 32

(1984): 871-883.

[66] G.Picci, G.Prati, Blind equalization and carrier recovery using a stop –and-go decision

derected algorithm, IEEE Transactions on Communications 35 (1987): 877-887.

[67] T.Nguyen, Z.Ding, Blind CMA beamforming for narrowband signals with multipath

arrivals, International Journal of Adaptive Control and Signal Processing, 12 (2) (1998): 157-

172.

84

[68] J.K. Tugnait, T. Li, Bling asynchronous multiuser CDMA receivers for ISI channels

using code-aided CMA, IEEE Journal on Selected Areas Communication 19 (2001): 1520-

1530.

[69] C. Xu, G. Feng, and K.S Kwak, A modified constrained constant modulus approach to

blind adaptive multiuser detection, IEEE Transactions on Communications 49 (2001): 1642-

1648.

[70] S. A. Sheikh, P. Fan, Two efficient adaptively varying modulus blind equalizers: AVMA

and DM/AVMA, Digital Signal Processing 16 (2006): 832–845

[71] A. Zaouche, I. Dayoub, J.M. Rouvaen, Baud-spaced constant modulus blind equalization

via hybrid genetic algorithm and generalized pattern search optimization, International

Journal of Electronics and Communication (AEU) 62, (2008): 122 – 131

[72] M. Rupi, P. Tsakalides, E. D. Re, C. Nikias, Constant modulus blind equalization based

on fractional lower-order statistics, Signal Processing 84 (2004): 881 – 894

[73] Z. Ding, R.A. Kennedy, B.D.O Anderson, C.R Johnson, III-convergance of Godard blind

equalizers in data communication systems, IEEE Transactions on Communications 39

(1991): 3-15.

[74] C.R Johnson, Admissiblity in blind adaptive channel equalization, IEEE Control System

Magazine (1991): 3-15.

[75] O.Shalvi and Weinstein, New criteria for blind deconvolution of nonminimum phase

systems (channels), IEEE Information Theory, 36 (2) (1990): 312-321.

[76] W.A. Gardner, A new method of Channel identification, IEEE Transactions on

Communications 39 (6) (1991): 813-817.

85

[77] L. Tong, G. Xu, T.Kailath, A new approach to blind identification ad equalization of

multipath channels, in Proceddings of. Asilomar Conference on Signals, systems, Computers,

(1991): 856-860.

[78] L. Tong, G. Xu, T. Kailath, Blind Identification and Equalization using Spectral

Measures, Part II: A Time Domain Approach, In W.A Gardner Ed., editor, Cyclostationary in

Communications and Signal Processin. IEEE Press, 1993.

[79] B. Xerri, B. Borloz, An iterative method using conditional second order statistics applied

to blind source separation problem, IEEE Transactions on Signal Processing, 52 (2004):.313-

328.

[80] Z. xu, P.Liu, X.Wang, Blind multiuser detection from MOE to subspace methods, IEEE

Transactions on Signal Processing, 52 (2004): 510-524.

[81] F.A. Dietrich, W.Utschick, Pilot assisted channel equalization based on second order

statistics, IEEE Transactions on Signal Processing, 53 (2005): 1178-1193.

[82] K. Rahbar, J.P. Reilly, J.H Manton, Blind identification of MIMO FIR systems driven by

quasistationary source using second order statistics: a frequency domain approach, IEEE

Transactions on Signal processing, 52 (2004): 406-417.

[83] J.K. Tugnait, W.Luo, Blind identification of time varying channels, using multistep linear

predictors, IEEE Transactions on Signal Processing, 52 (2004): 1739-1749.

[84] Y.Huang, P.M Djuric, A blind partical filtering detector of signals transmitted over flat

fading channels, IEEE Transactions on Signal Processing, 52 (2004): 1891-1900.

[85] A. Napolitano, M.Tanda, Doppler channel bling identification for non cicrcular

transmission in multiple access systems, IEEE Transactions on Communications, 52 (2004):

2073-2078.

86

[86] M.Castella, J.C. Pesquet, A.P Petropulu, Family of Frequency and Tiime Domain

Contrasts for Blind Separation of Convolutive Mixtures of Temporally Dependent Signals,

IEEE Transactions on Signal Processing 53 (2005): 107-120.

[87] Y. Hua, M. Wax, strict identifiability of multiple FIR channels driven by an unknown

arbitrary sequences, IEEE Transactions on Signal Processing, 44 (1996): 756-759.

[88] M.L. Gurreli, C.L.Nikias, EVAM: An eigenvector-based deconvolution of input coloured

signals, IEEE Transactions on Signal Processing, 43 (1995): 134-149.

[89] Y. Hua, Fast maximum likelihood for blind identification for multiple FIR channels, ,in

in Proceddings of. Asilomar Conference on Signals, systems, Computers, (1994).

[90] G. Dong, R. Liu, Adaptive blind channel identification, in Proceddings of. 1995 IEEE

International Conference on communications, Dallas, TX, (June 1996): 828-831.

[91] G. Dong. R. Liu, An orthogonal learning rule for null pspace tracking with

implemantation to blind two-channel identification, IEEE Transactions on Circuits Systems I

45 (1998): 26-33.

[92] S. Haykin, Adaptive filter Theory, 4th

edition, Pearson Education Asia, pp. 803-804.

[93] E. Moulines, P.Duharnel, J.f Cardoso, S. Mayrargue, Subspace-methods for the blind

identification of multichannel FIR filters, presented at ICASSP’94 conference Adelaide,

Australis, Apr. 1994.

[94] E. Moulines, P. Kuharnel, J.F. Cardoso, S .Mayrargue, Subspace-methods for the blind

identification of multichannel FIR filters, IEEE Transactions on Signal Processing 43 (1995):

516-525.

87

[95] L. Tong, Q. Zhao, Joint order detection and channel estimation by least squares

smoothing, presented at 30th

Conference Information Sciences and Systems, Princeton, NJ.

Mar. 1998.

[96] L. Tong, G, Xu, T. Kailath, Blind identification and equalization based on second order

statistics: A time domain approach, IEEE Transactions on Information Theory, 40 (1994):

340-349.

[97] K. Abed-Meraim, E. Moulines, P. Loubaton, Prediction error method for second- order

blind identification, IEEE Transactions on Signal Processing, 45 (1997): 694-705.

[98] R.L Valcarce, S.Dasgupta, Blind channel equalization with coloured sources based on

second order stastistcs: A linear prediction approach, IEEE Transactions on Signal

Processing, 49 (2001): 2050-2059.

[99] G.K Kaleh, R. Vallet, Joint parameter estimation and symbol detection for linear or

nonlinear unknown dispersive channels, IEEE Transactions on Communications, 42 (1994):

2406-2413.

[100] E.Qeinstein, M Feder, A. Oppenheim, Sequential algorithms for parameter estimation

based on the Kullback-Leibler information measure, IEEE Transactions on Signal

Processing, 38 (1990): 1652-1654.

[101] G. Harikuma, Y.Bresler, Analysis and comparitive evaluatio of techniques for

multichannel blind equalization, in Proceedings of 8th

IEEE Signal Processing Workshop

Statistical and Array Signal Processing , , Grece, (June 1996):332-335.

[102] C.B Papadias, Methods for blind equalization and identify-cation of linear channel’,

Ph.D. dissertation, Ecole Nationale Superieure des Telecommunications, Paris, France, Mar.

1995.

88

[103] M. Feder, J.A. Catipovec, Algorithms for joint channel estimating and data recovery-

Application to underwater communications’, IEEE Journal on Oceanic Engineering, 16

(1991): 42-55.

[104] S. Amari, A. Cichocki, Adaptive blind signal processing- Neural network approaches,

Proceedings of the IEE, 86 (10) (1998): 2026-2048.

[105] Y.Fang, T.W.S Chow, K.T Ng, Linear neural network based blind equalization, Signal

Processing 76 (1999}: 37-42.

[106] S. Chen, C. Cowan, P. Grant, Orthogonal least squares learning algorithm for radial basis

function networks, IEEE Transactions on Neural Networks 2 (2) (1991): 302 - 309

[107] S. Chen, G. Gibson, C. Cowan, P. Grant, Reconstruction of binary signals using an

adaptive radial-basis-function equalizer, Signal Processing 22 (1991): 77-93.

[108] J. Choi, S. Bang, B. Sheu, A programmable analog VLSI neural network processor for

communication receivers, IEEE Transactions on Neural Networks 4 (3) (1993): 484-495.

[109] J. Cid-sueiro, A. Figueiras-Vidal, Digital equalization using modular neural networks: an

overview, in: E. Biglieri, M. Luise (Eds.), Signal Processing for Digital Communications,

Springer, London, 1996.

[110] W.R. Kirkland, D.P. Taylor, Neural network channel equalization, in: B. Yuhas, N.

Ansari (Eds.), Neural Networks in Telecommunications, Kluwer Academic Publishers,

Dordrecht, (1994): 143-172.

[111] N. Lo, H. Hafez, Neural network channel equalization, Proceedings of International Joint

Conference on Neural Networks, Vol. 2, (1992): 981-986.

[112] S. Chen, G. Gibson, C. Cowan, P. Grant, Adaptive equalization of finite non-linear

channels using multi layer perceptron, Signal Processing 20 (1990): 107-119.

89

[113] A. Hussain, J. Soraghan, T. Durrani, A new adaptive functional-link neural network-

based DFE for overcoming co-channel interference, IEEE Transactions on Communications

45 (11) (1997): 1358-1362.

[114] G. D. Veciana, A. Zakhor, Neural net-based continuous phase modulation receivers,

IEEE Transactions on Communications 40 (8) (1992): 1396 - 1408

[115] S. Chen, S. McLaughlin, B. Mulgrew, Complex-valued radial basis function networks,

Part I: Network architecture and learning algorithms, Signal Processing 35 (1) (1994): 19-31.

[116] S. Chen, S. McLaughlin, B. Mulgrew, Complex-valued radial basis function networks,

Part II: Application to digital communication channel equalization, Signal Processing 36 (2)

(1994): 175-188.

[117] Z. Xiang, G. Bi, A new polynomial perceptron based 64QAM cellular mobile

communications receivers, IEEE Transactions on Signal Processing 43 (12) (1995): 3094-

3098.

[118] L. Chua, L. Yang, Cellular neural networks: theory, IEEE Transactions on Circuits

Systems 35 (1988): 1257-1272.

[119] B. Sheu, S. Bang, R. Chang, A cellular neural network with optimized performance for

wireless communication receivers, Proceedings of WCNN'95, Washington, DC, (June 1995):

II-660-II-664.

[120] S.H. Bang, B.J. Sheu, J. Choi, Programmable VLSI neural network processors for

equalization of digital communication channels, Proceedings of International Workshop on

applications of Neural Networks to Telecommunications, LEA Publishers, 1993, pp. 1-12.

[121] R. Williams, D. Zipser, A learning algorithm for continually running fully recurrent

neural networks, Neural Computation 1 (1989): 270-280.

90

[122] G. Kechriotis, E. Zervas, E. Manolakos, Using recurrent neural networks for adaptive

communication channel equalization, IEEE Transactions on Neural Networks 5 (2) (1994):

267-278.

[123] R. Parisi, E. Di Claudio, G. Orlandi, B. Rao, Fast adaptive digital equalization by

recurrent neural networks, IEEE Transactions on Signal Processing 45 (11) (1997): 2731-

2739.

[124] B. Juang, S. Katagiri, Discriminative learning for minimum error classification, IEEE

Transactions on Signal Processing 40 (1992): 3043 - 3054

[125] T. Adali, X. Liu, M.K. Sonmez, Conditional learning with neural networks and its

application to channel equalization, Transactions on Signal Processing 45 (4) (1997): 1051 -

1064

[126] T. Adali, M.K. Sonmez, K. Patel, On the dynamics of the LRE algorithm: a distribution

learning approach to adaptive equalization, Proceedings of IEEE ICASSP'95, Detroit, MI,

(April 1995): 929-932.

[127] T. Kohonen, E. Oja, O. Simula, A. Visa, J. Kangas, Engineering applications of the self-

organizing map, IEEE Proceedings, (1996): 1358-1384.

[128] A. Guntsch, M. Ibnkahla, G. Losquadro, M. Mazzella, D. Roviras, A. Timm, ‘EU's R&D

activities on third generation mobile satellite systems (S-UMTS), IEEE Communication

Magazine (1998): 104 - 110

[129] S. Bouchired, Equalization of time-varying non-linear channels using neural networks:

application to the satellite mobile channel, Ph.D. Thesis, INP, Toulouse, France, 1999.

91

[130] S. Bouchired, M. Ibnkahla, D. Roviras, F. Castanieh, Equalization of satellite UMTS

channels using neural network devices, Proceedings of IEEE ICASSP'99, Phoenix, May

1999.

[131] H. Lin, K. Yamashita, Hybrid simplex genetic algorithm for blind equalization using

RBF networks, Mathematics and Computers in Simulation 59 (2002); 293–304

[132] S. Han, W. Pedrycz, C. Han, Nonlinear Channel Blind Equalization Using Hybrid

Genetic Algorithm with Simulated Annealing, Mathematical and Computer Modelling 41

(2005): 697-709.

[133] R. Pandey, Complex-Valued Neural Networks for Blind Equalization of Time-Varying

Channels, International Journal of Signal Processing 1 (1) (2004): ISSN: 1304-4494

[134] W. Chagra, F. Bouani, R. B. Abdennour, M. Ksouri, G. Favier, Equalization with

decision delay estimation using recurrent neural networks, Advances in Engineering

Software 36 (2005): 442–447.

[135] L. Zhang, X. Zhang, MIMO Channel Estimation and Equalization Using Three-Layer

Neural Networks with Feedback, Tsinghua science and technology, 12 (6) (2007): 658-662

[136] J. Lee, R. Sankar, Theoretical derivation of minimum mean square error of RBF based

equalizer Signal Processing 87 (2007): 1613–1625

[137] H. Zhao, J. Zhang, Functional link neural network cascaded with Chebyshev orthogonal

polynomial for nonlinear channel equalization, Signal Processing 88 (8) (2008): 1946–1957

[138] K. Burse, R. N. Yadav, S. C. Shrivastava, Channel Equalization Using Neural Networks:

A Review, IEEE Trans. On Systems, man and cybernetics-Part C: Applications and

Reviews, 40 (3) (2010): 352-357.

92

[139] R. H. Abiyev, O. K. T. Alshanableh, F. Mamedov. A Type-2 Neuro-fuzzy System Based

on Clustering and Gradient Techniques Applied to System Identification and Channel

Equalization. Applied Soft Computing, 11 (1) (2011); 1396–1406.

[140] T. Low; T. Lee; H.K.Lim, A methodology for neural network training for control of

drives with nonlinearities, IEEE Transactions on Industrial Electronics, 40 (2) (1993): 243-

249.

[141] V.W. Porto, D.B. Fogel, Alternative neural network training methods [active sonar

processing], IEEE Experts 10 (3) (1995): 16-22.

[142] L. Chih-Min, C. Hsu, Supervisory recurrent fuzzy neural network control of wing rock

for slender delta wings, IEEE Transactions on Fuzzy Systems, 12 (5) (2004): 733-742.

[143] F.H.C. Tivive, A. Bouzerdoum, Efficient training algorithms for a class of shunting

inhibitory convolutional neural networks, IEEE Transactions on Neural Networks, 16 (3)

(2005): 541-556.

[144] O. Ludwig, U. Nunes, Novel Maximum-Margin Training Algorithms for Supervised

Neural Networks, IEEE Transactions on Neural Networks, 21(6) (2010): 972-984,

[145] S. Razavi, B.A. Tolson, A New Formulation for Feedforward Neural Networks, IEEE

Transactions on Neural Networks, 22 (10) (2011): 1588-1598,

[146] X. Wang, Y. Huang, Convergence Study in Extended Kalman Filter-Based Training of

Recurrent Neural Networks, IEEE Transactions on Neural Networks, 22 (4) (2011): 588-600.

[147] I. E. Mulia, H. Tay, K. Roopsekhar, P. Tkalich, Hybrid ANN–GA model for predicting

turbidity and chlorophyll-a concentrations, Journal of Hydro-environment Research, 7 (4)

(2013): 279-299

93

[148] K. Benyelloul, H. Aourag, Bulk modulus prediction of austenitic stainless steel using a

hybrid GA -ANN as a data mining tools, Computational Materials Science, 77 (2013): 330-

334

[149] M. Yaghini, M. M. Khoshraftar, M. Fallahi, A hybrid algorithm for artificial neural

network training, Engineering Applications of Artificial Intelligence, 26 (2013) : 293-301

[150] M. Geethanjali, S. Mary, R. Slochanal, R. Bhavani, PSO trained ANN -based differential

protection scheme for power transformers, Neurocomputing, 71 (2008) : 904-918

[151] N. A. Algeelani, M. Afendi, M. Piah, Z. Adzis, M. A. Algeelani, Hybrid regrouping PSO

based wavelet neural networks for characterization of acoustic signals due to surface

discharges on H.V. glass insulators, Applied Soft Computing, 13 (12), (2013) : 4622-4632

[152] G. Das, P. K. Pattnaik, S. K. Padhy, Artificial Neural Network trained by Particle Swarm

Optimization for non-linear channel equalization, Expert Systems with Applications, 41 (7),

(2014) : 3491-3496

[153] V. Bergh, A.P. Engelbrecht, A new locally convergent particle swarm optimizer,

Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, (2002):

96-101

[154] M.M. Eusuff, K. Lansey, Optimization of water distribution network design using the

shuffled frog leaping algorithm. Journal of Water Resource Planning Management 129 (3)

(2003): 210–225.

94

[155] X. Cheng, X. Zhang, L. Zhao, A. Deng, Y. Bao, Y. Liu, Y. Jiang, The application of

Shuffled Frog Leaping Algorithm to Wavelet Neural Networks for acoustic emission source

location. Comptes Rendus Mecanique, 342 (2014): 229-233.

[156] A. Kavousifard, H. Samet, A Novel Method Based on Modified Shuffled Frog Leaping

Algorithm and Artificial Neural Network for Power System Load Prediction, Emerging

Intelligent Technologies in Industry, Studies in Computational Intelligence 369 (2011): 35-

46.

[157] N. Wang, X. Li, X. Chen, Fast three-dimensional Otsu thresholding with shuffled frog-

leaping algorithm, Pattern Recognition Letters, 31 (2010): 1809–1815

[158] G. Altın, R. K. Martin, Bit-error-rate-minimizing channel shortening using post-FEQ

diversity combining and a genetic algorithm, Signal Processing, 91 (2011): 1021–1031.

[159] S. Saha, S.S. Pathak, S. Chakrabarti,, Comprehensive Learning Particle Swarm

Optimization based TSK structure identification and its application in OFDM receiver for

nonlinear channel equalization, 2011, National Conference on Communications, pp. 1 - 5

[160] G.A. Carpenter, S. Grossberg, Adaptive Resonance Theory, In Michael A. Arbib (Ed.),

The Handbook of Brain Theory and Neural Networks, Second Edition Cambridge, MA: MIT

Press, (2003): 87-90.

[161] D. Wienke, L. Buydens, Adaptive resonance theory based neural network for supervised

chemical pattern recognition (FuzzyARTMAP) Part 1: Theory and network properties,

Chemometrics and Intelligent Laboratory Systems, 32, (1996): 151-164

[162] G. Li, Z. Yan, J. Wang, A one-layer recurrent neural network for constrained nonconvex

optimization, Neural Networks, 61 (2015): 10-21

95

[163] M. S. Ali, Stability of Markovian jumping recurrent neural networks with discrete and

distributed time-varying delays, Neurocomputing, 149, Part C, (2015): 1280-1285

[164] T. Segreto, A. Simeone, R. Teti, Principal component analysis for feature extraction and

NN pattern recognition in sensor monitoring of chip form during turning, CIRP Journal of

Manufacturing Science and Technology, 7 (3) (2014): 202-209

[165] D. Z. Antanasijević, M. Đ. Ristić, A. A. P. Grujić, V. V. Pocajt, Forecasting GHG

emissions using an optimized artificial neural network model based on correlation and

principal component analysis, International Journal of Greenhouse Gas Control, 20 (2014):

244-253

[166] S. Chaki, A. K. Verma, A. Routray, W. K. Mohanty, M. Jenamani, Well tops guided

prediction of reservoir properties using modular neural network concept: A case study from

western onshore, India, Journal of Petroleum Science and Engineering, 123 (2014): 155-163.

[167] Y. Ding, Q. Feng, T. Wang, X. Fu, A modular neural network architecture with concept,

Neurocomputing, 125 (2014): 3-6

[168] H. Asgari, Y. S. Kavian, A. Mahani, A Systolic Architecture for Hopfield Neural

Networks, Procedia Technology, 17 (2014): 736-741

[169] S.S. Young, P.D. Scott, N.M. Nasrabadi, Object recognition using multilayer Hopfield

neural network, IEEE Transactions on image Processing, 6 (3) (1997): 357 – 372

[170] W. Hao, Z. Xianmin, K. Yongcong, O. Gaofei, X. Hongwei, Solder joint inspection

based on neural network combined with genetic algorithm, Optik - International Journal for

Light and Electron Optics, 124 (20) (2013): 4110-4116

[171] Y. Huan, G. Feng, B. Wang, Y. Ren, Q. Fei, Quantitative analysis of cefalexin based on

artificial neural network combined with modified genetic algorithm using short near-infrared

96

spectroscopy, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 109

(2013): 308-312

[172] F. Yu, X. Xu, A short-term load forecasting model of natural gas based on optimized

genetic algorithm and improved BP neural network, Applied Energy, 134 (2014): 102-113

[173] A. P. Piotrowski, J. J. Napiorkowski, Optimizing neural networks for river flow

forecasting– evolutionary computation methods versus the Levenberg–Marquardt approach,

Journal of Hydrology, 407 (2011): 12-27

[174] P.A. Gutiérrez, C. Hervás, M. Carbonero, J.C. Fernández, Combined projection and

kernel basis functions for classification in evolutionary neural networks, Neurocomputing, 72

(2009): 2731-2742

[175] O. Castillo, P. Melin, E. Ramírez, J. Soria, Hybrid intelligent system for cardiac

arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks combined

with a fuzzy system, Expert Systems with Applications, 39 (3) (2012): 2947-2955

[176] R. Cheng, Y. Bai, A novel approach to fuzzy wavelet neural network modeling and

optimization, International Journal of Electrical Power & Energy Systems, 64 (2015): 671-

678

[177] G. Liao, Hybrid Improved Differential Evolution and wavelet neural network with load

forecasting problem of air conditioning, International Journal of Electrical Power & Energy

Systems, 61 (2014): 673-682

[178] X. Chen, C. Shen, W. Zhang, M. Tomizuka, Y. Xu, K. Chiu, Novel hybrid of strong

tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages,

Measurement, 46 (10) (2013): 3847-3854

97

[179] S. Poungponsri, X. Yu, An adaptive filtering approach for electrocardiogram (ECG)

signal noise reduction using neural network, Neurocomputing, 117 (2013): 206-213

[180] L. Tsoukalas, R. Uhrig, Fuzzy and Neural Approaches in Engineering, Wiley-

Interscience, New York, 1997.

[181] S. Haykin, Neural Networks: a Comprehensive Foundation, IEEE Press, New York,

1994.

[182] G. Cybenko, Approximation by superposition of a sigmoidal function’, Math. Control

Signals Systems 2 (1989): 303-314.

[183] K. Hornik, Multilayer feedforward networks are universal approximators, Neural

Networks 2 (1989): 359-366.

[184] R.P. Lippmann, An introduction to computing with neural nets, IEEE ASSP Mag. April’

1987, pp 4-22.

[185] D.E. Rumelhart, G.E. Hinton, R.J. Williams, ‘Learning internal representations by error

propagation’, in: D.E. Rumelhart, J.L. McClelland (Eds.), Parallel Distributed Processing,

Vol. 1, MIT Press, Cambridge, MA, 1986, pp. 318-362.

[186] T. Clarke, Generalization of neural networks to the complex plane, International Joint

Conference on Neural Networks, 2 (1990): 435-440.

[187] H. Leung, S. Haykin, The complex back propagation algorithm, IEEE Transactions on

Signal Processing 39 (9) (1991): 2101-2104.

[188] K. Fukumizu, Geometry of neural networks: Natural gradient for learning, Chapter 17,

Handbook of Biological Physics, 4 (2001): 731-769

[189] S. Haykin, Neural Networks: a Comprehensive Foundation, IEEE Press, New York,

1994.

98

[190] J. Jiang, Qian Su, Ming Li, Mengnan Liu, Liyuan Zhang, An improved shuffled frog

leaping algorithm, Journal of Informational and Computational Science, 10 (14) (2013):

4619-4626.

[191] M. Yaghini, M. M. Khoshraftar, M.Fallahi, A hybrid algorithm for artificial neural

network training, Engineering Applications of Artificial Intelligence, 26 (1) (2013): 293-301

[192] M. N. Seyman, N. Taşpınar, channel estimation based on neural network in space time

block coded MIMO–OFDM system, Digital Signal Processing, 23 (1) (2013): 275-280.

[193] X. Ruan, Y. Zhang, Blind sequence estimation of MPSK signals using dynamically

driven recurrent neural networks, Neurocomputing, 129 (2014): 421-427

[194] A. Rizaner, Radial basis function network assisted single-user channel estimation by

using a linear minimum mean square error detector under impulsive noise, Computers &

Electrical Engineering, 39 (4) (2013): 1288-1299.

[195] H.K. Sahoo, P.K. Dash, N.P. Rath, NARX model based nonlinear dynamic system

identification using low complexity neural networks and robust H∞ filter, Applied Soft

Computing, 13 (7) (2013): 3324-3334

[196] D. J. Montana and L. Davis, Training Feedforward Neural Networks Using Genetic

Algorithms, Machine Learning, pp. 762-767, http://ijcai.org/Past%20Proceedings/IJCAI-89-

VOL1/PDF/122.pdf

[197] A. Blanco, M. Delgado, M.C. Pegalajar, A real-coded genetic algorithm for training

recurrent neural networks, Neural Networks 14 (2001): 93-105.

[198] D. Kim, H. Kim, D. Chung, A Modified Genetic Algorithm for Fast Training Neural

Networks, Lecture Notes in Computer Science, 3496 (2005): 660-665.

99

[199] A.S. Rakitianskaia, A. P. Engelbrecht, Training feedforward neural networks with

dynamic particle swarm optimization, Swarm Intelligence, 6 (3) (2012): 233-270.

[200] I. Vilovic, N. Burum, D. Milic, , Using particle swarm optimization in training neural

network for indoor field strength prediction, ELMAR, International Symposium , (2009):

275,278,

[201] R. Su; L. Kong; S. Song; P. Zhang; K. Zhou; J. Cheng, A New Ridgelet Neural Network

Training Algorithm Based on Improved Particle Swarm Optimization, Third International

Conference on Natural Computation, 3, (2012): 411-415.

[202] R. K. Jatoth, M. S. Vaddadi, S. Anoop, An intelligent functional link artificial neural

network for channel equalization, ISPRA'09 Proceedings of the 8th WSEAS international

conference on Signal processing, (2009): 240-245

[203] G. Das, P.K. Pattnaik, S. K. Padhy, Artificial Neural Network trained by Particle Swarm

Optimization for non-linear channel equalization, Expert Systems with Applications, 41 (7)

(2014): 3491-3496

[204] G. R. Patra, S. Maity, S. Sardar, S. Das, Nonlinear Channel Equalization for Digital

Communications Using DE-Trained Functional Link Artificial Neural Networks,

Communications in Computer and Information Science 168 (2011): 403-414.

[205] N. Karaboga, Digital IIR filter design using differential evolution algorithm, EURASIP

Journal on Applied Signal Processing, 8 (2005): 1269-1276.

[206] V. Bergh, F. Engelbrecht, A new locally convergent particle swarm optimizer,

Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (2002):

96-101

100

[207] J. Liu, J. Lampinen, On setting the control parameter of the differential evolution method,

Proc. 8th International Conference on Soft Computing (MENDEL 2002), (2002): 11-18.

[208] B. Tripathy, S. Dash, S. K. Padhy, Dynamic Task Scheduling using Directed Neural

Network, Elsevier Journal of Parallel and Distributed Computing, 75(2015): 101-106..

[209] D. Zou, H. Liu, L. Gao, S. Li, Directed searching optimization algorithm for constrained

optimization problems, Expert Systems with Applications 38 (2011): 8716–8723.

[210] H. Zhao, X. Zeng, J. Zhang, T. Li, Y. Liu, D. Ruan, Pipelined functional link artificial

recurrent neural network with the decision feedback structure for nonlinear channel

equalization, Information Sciences 181 (2011): 3677–3692