自学习神经元及自学习BP网络  

SELF-EARNING NEURONS AND SELF LEARNING BP NETWORKS

作　　者：赖晓平[1] 周鸿兴[2] 

机构地区：[1]山东大学威海分校控制工程系,威海264209 [2]山东大学数学与系统科学学院,济南250100

出　　处：《信息与控制》1999年第6期407-415,共9页Information and Control

基　　金：国家自然科学基金资助项目!项目批准号:69774002

摘　　要：本文针对现有人工神经元及ＢＰ网络的缺点，从实现角度提出一种新型神经元及新型ＢＰ网络——自学习神经元及自学习ＢＰ网络．自学习神经元的突出特点之一是它的内部有正向通道、反向通道及学习器，因而能够独立完成信息的正向传播、误差的反向传播及神经元参数的修正．由自学习神经元组成的自学习ＢＰ网络可以真正做到正向传播信息、反向传播误差及学习的并行化．本文还考虑了自学习ＢＰ网络的学习问题，提出一种新的学习策略．我们的仿真结果表明这种学习策略有很好的学习效果．A new king of neurons and new kind if BP networks, self learning neurons and self learning BP networks, are presented in this paper with a wiew to their implementation. A self learning BP network is composed of self learning neurons. One of the prominent characteristics of the self learning neuron is thatit has a forward channel, a backward channel and a leamer, This makes each neuron in a self learning BP network accomplish the forward propagation of message, backward propagation of errors, and modification of its parameters independently, and hence makes the network implement parallel computing easity. A new training policy for BP networks is also presented in this paper. Simulation results demonstrate the effectiveness of the policy.

关 键 词：自学习神经元 自学习BP网络 学习策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]