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一种前馈神经网络改进的新型分层算法
A NEW ALGORITHM OF OPTIMIZATION LAYER BY LAYER ABOUT FEEDFORWARD NEURAL NETWORKS
【摘要】 本文提出了实三层前馈网络的一种新型学习算法。该算法采用的是分层优化方法,将隐层的非线性神经元线性化,线性化产生的误差通过罚项而受到限制。分层优化使得每一层权值整体优化,而与另一层无关,这样使得整个优化过程更为有效。
【Abstract】 In this paper, a new algorithm for training of 3-layered feedforward neural network is proposed, which is based on a linearization of the nonlinear processing neurons and the optimization of the 3-layered feedforward neural networks layer by Layer. The weights in each layer are updated dependent on each other in one iteration epoch, but separately from other layers.
【关键词】 分层优化算法;
前馈神经网络;
线性化误差;
罚项;
【Key words】 Optimization laryer by layer; Feedforward Neural Networks; Linearization error; Penalty Term;
【Key words】 Optimization laryer by layer; Feedforward Neural Networks; Linearization error; Penalty Term;
- 【文献出处】 模式识别与人工智能 ,Pattern Recognition and Artificial Intelligence , 编辑部邮箱 ,2000年01期
- 【分类号】TP183
- 【被引频次】4
- 【下载频次】63