关于三层前馈神经网络隐层构建问题的研究

On Constructing a Hidden Layer for Three-Layered Feedforward Neural Networks

针对最佳平方逼近三层前馈神经网络模型 ,分析了与隐层单元性能相关的表示空间与误差空间、目标空间与耗损空间的作用 ,提出了按网络生长方式构建隐层时隐单元选择准则和评价方法 研究结果表明 :隐单元选取策略应遵循其输出向量有效分量位于误差空间、回避耗损空间和尽可能趋向于极大能量方向的原则 ,这一结果与隐单元采用什么激发函数无关 ,也允许各隐单元采用不同激发函数 网络的隐层性能评价可以通过隐层品质因子、隐层有效系数、隐单元剩余度来进行 。

The hidden unit performance related spaces, i e representation space and error space, target space and expended space, are analyzed according to the model of least squares approximation feedforward neural networks, and further more, the criteria and the evaluation methods for the performance of hidden units are proposed It is revealed that the efficient component of output vector of a hidden unit should be lying in error space, avoiding expended space and closing the direction of maximum energy, which are independent of the nonlinear functions used by the units, and it is permitted that different units have different activation functions The quality factor of the hidden layer, efficient coefficient of the hidden layer, the redundancy of hidden units and the evaluation factor of the hidden layer for total evaluation, are proposed for the performance evaluation of the hidden layer