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Method of Bayesian Network Parameter Learning Base on Improved Artificial Fish Swarm Algorithm

被引：0
|
作者：
Wang, Yan [1 ]
Zhang, Liguo [2 ]
机构：
[1] North China Elect Power Univ, Dept Comp, Baoding, Peoples R China
[2] Agr Univ Hebei, Coll Informat Sci & Technol, Baoding, Peoples R China
来源：
COMPUTING AND INTELLIGENT SYSTEMS, PT IV | 2011年 / 234卷
关键词：
Bayesian network; parameter learning; Artificial fish swarm algorithm; genetic algorithm; Noisy-And; Noisy-Or;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Bayesian network is an effective model to solve uncertainty problem. Parameter learning is an important step for building a Bayesian network, and its performance directly affects the network's accuracy. In this paper, artificial fish swarm algorithm is introduced into the parameter learning of Bayesian network composed of Noisy-Or and Noisy-And nodes, and the global search capability is also improved by genetic algorithm. The experimental results show that the improved artificial fish swarm algorithm can learn the parameter better, with the characteristic of rapid optimization speed, good global convergence and insensitivity to initial value.
引用
收藏
页码：508 / +
页数：2
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