Hidden Layer Performance Evaluation for Feedforward Neural Networks Based on Hidden Layer Growing Strategy
基于隐层生长策略的前馈神经网络隐层性能评测

A hidden layer performance evaluation method is discussed according to the model of least-squares approximation feedforward neural networks based on hidden layer growing strategy. Firstly, some spaces which affect the performance of feedforward neural networks are analyzed and four concepts of subspace, i.e. representation space, error space, target space and expend, are introduced. The error compensation performance of the hidden unit is analyzed. Finally, evaluation parameter of hidden layer performance is proposed, and the rationality and validity of proposed method are validated by reviewing classical BP algorithm and orthogonal algorithm.