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THE COMPLEXITY OF LEARNING IN PLN NETWORKS

作     者:ZHANG, B ZHANG, LN ZHANG, H 

作者机构:ANHUI UNIVHEFEI 230039PEOPLES R CHINA 

出 版 物:《NEURAL NETWORKS》 (Neural Netw.)

年 卷 期:1995年第8卷第2期

页      面:221-228页

核心收录:

学科分类:1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术(可授工学、理学学位)] 10[医学] 

主  题:LEARNING ALGORITHM MARKOV CHAINS TRANSITION MATRIX ABSORBING STATE STABLE STATE PLN NETWORK 

摘      要:In this paper, the complexity of learning in the feedforward PLN network is investigated by using Markov chain theory, when its training samples are incomplete (i.e., a network with hidden nodes). We present a learning algorithm. A formula for computing the average number of steps that the learning algorithm converges is obtained when the PLN network exists a solution. In the probabilistic sense, the completeness of the learning algorithm is proved. Some computer simulations are given to verify the analysis.

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