Bayesian training of neural networks using genetic programming

作     者：Marwala, Tshilidzi 

作者机构：Univ Witwatersrand Sch Elect & Informat Engn ZA-2050 Johannesburg South Africa 

出 版 物：《PATTERN RECOGNITION LETTERS》 (模式识别快报)

年 卷 期：2007年第28卷第12期

页      面：1452-1458页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Bayesian framework evolutionary programming neural networks 

摘      要：Bayesian neural network trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. The algorithm proposed here has the ability to learn using samples obtained from previous steps merged using concepts of natural evolution which include mutation, crossover and reproduction. The reproduction function is the Metropolis framework and binary mutation as well as simple crossover, are also used. The proposed algorithm is tested on simulated function, an artificial taster using measured data as well as condition monitoring of structures and the results are compared to those of a classical MCMC method. Results confirm that Bayesian neural networks trained using genetic programming offers better performance and efficiency than the classical approach. (c) 2007 Elsevier B.V. All rights reserved.