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.