The selected membership function made neural network weight values have definite knowledge meaning, and the input characteristic variables were translated into fuzzy variables by fuzzy layer.
选取的隶属函数使神经网络权值有一定的知识表示意义，并通过模糊化层将输入特征量转化为模糊量。
The optimum combinations adopting different numbers of characteristic parameters have been obtained by analyzing the main variables of all the input parameters.
对全部输人特征参数进行了主变量分析，给出了采用不同数量特征参数的优化组合方案。
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