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So, the key points of the curve to control the shape are confirmed after anti-normalizing all of the

data to restore the coordinates. The more enough key points being setup, the curve will be more

smooth, and the target shape is more clear.

Analysis and discussion

Based on BP neural network, a body shape formative element of the solution model is established,

designers can precisely control any segment in this model only by changing the input values

according to the actual situation in specific application. For example, an investigation discovers that

users prefer such a model: weighing coefficient of sport style is 0.6, lively sense 0.45, futuristic sense

0.3, mellow style 0.65. Then designers may input these evaluation values and basic segments of the

coordinate into the BP network inverse-model to deduce the precise location of critical points on

target region. The core idea in conversion is "clarify user’s preference→ convert to BP model

parameter→ get the normalization value of prediction→ revert to the original coordinates." The

quantitative solution method is an important complement to designer kansei feeling.

Here is another inference based on the above cases: The design goal is to predict the sample 7

(No. 35 of samples) windshield curve shape. Market research shows people’s expectation are sportstyle is 60%, lively sense 45%, futuristic sense 30%, mellow style 65%. Input independent variables

such as f 1, f 2, f 3, f 4 and the known key points in other regions into the inverse model, then the

dependent variable, Windshield of the two control points (4 values) are outputted. The data is shown

as follows in Table 4.

Table 4 The data conversion between experimental evaluation value and the predictive value in

the inverse BP model

conversion of evaluation value conversion of Prediction value

Perceptual

Percentage[-3

，

3] Interval normalizationOriginal

coordinates

f1 60 % 1.80 x17 0.4132 27.7968

f2 45 % 1.35 x18 0.6255 29.0072

f3 30 % 0.90 x19 0.7833 33.4283

f4 65 % 1.95 x20 0.5029 30.7526

Fig.4 The restored map of the inverse model

Based on the definition of the above table, points P9 and P10 could be tracked in the coordinates

system, so the displacement contours of the Windshield trend and the specific location is clear

controlled by these two points. Details are shown in Fig.4. The black line is the original patterns and

the red one is for the prediction. In this way, entering a known ideal target (the user's evaluation)

could gain the design details (each key points for control segments).Theoretically, as long as

perfecting the training of BP neural network model and obtaining correspondence between

Advanced Materials Research Vols. 118-120 751

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