This study of two new total hand simulating methods for knits uses fuzzy theory and neural networks. One method, a neural network system trained with a back-propaga tion algorithm, performs functional mapping between mechanical properties and the resulting total hand values of the fuzzy predicting method. The second method, a fuzzy-neural network system, uses the fuzzy membership function, weighted factor vector, and error back-propagation algorithm. The principal mechanical properties of stretchiness, bulkiness, flexibility, distortion, weight, and surface roughness of the knitted fabrics are correlated with experimentally determined Kawabata total hand values and fuzzy transformed overall hand values. Fuzzy and neural networks agree better with the subjective test results than the KES-FB system. The mechanical prop erties are fuzzified by fuzzy membership functions, then trained to predict the total hand value of outerwear knitted fabrics. In each case, the prediction error is less than the standard deviation of experimentation, and the optimum structure is investigated. These two systems, which use the Pascal programming language, produce objective ratings of outerwear knit fabrics.
This paper concentrated on the objective evaluation of total hand value in knitted fabrics using the theory of neural networks and the comparison of two methods. For the objective evaluation of overall hand feeling in knitted fabric, 47 kinds of weft-knitted and warp-knitted fabrics were manufactured. The optimum construction of neural networks was investigated through the change of layer and neuron number. For the comparison of the two methods, a subjective test was carried out. Two techniques, KES-FB system and neural network applied simulator, were compared using nine randomly selected knitted fabrics. These fabrics were used to show that the neural network adapted simulation method was in good agreement with subjective test results.
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