前传神经网络规模优化的快速剪枝策略及其应用

对多层前传神经网络规模的优化主要在于确定隐含层的节点数 .本文提出的快速剪枝法从分析网络隐含层的输出信息入手 ,用特征分析法解析地确定冗余而可剪去的隐节点数 ,并一次性地找出优化的隐节点数 ,同时将剪去节点的作用分配到保留的节点上 ,配置结构优化网络的初始连接权 ,以加速网络的训练进程 .这种快速剪枝的解析算法每步都有明确的数学机理 ,不仅优化速度快 ,而且稳定性好 .该算法应用于留兰香油的模式分类问题 ,效果令人满意 ,并显示出良好的健壮性和通用性

FAST PRUNING STRATEGY FOR NEURAL NETWORK SIZE OPTIMIZATION AND ITS APPLICATIONS

A new pruning strategy used to evaluate the optimal size of a multi-layered feed-forward neural network is proposed in this paper.The strategy is designed for arbitrary neural network converging after normal training.The output matrix of the hidden layer is calculated and used to extract characteristics.The number of the significant characteristics corresponds to the optimal number of hidden nodes.The redundant hidden nodes are deleted and their previous contributions are loaded on to the remaining ones.Therefore,the initial weights of the optimal structure can be mathematically calculated and assigned.After retrain the optimal structure, all the weights will be determined.Finally,this strategy is satisfactorily applied to solving a real pattern classification problem.The results indicate that this new method requires less computing time,and the accuracy of predictions is improved.Moreover,each stage of the proposed strategy possesses definite mathematical explanations so that the strategy could be generalized.