OPTIMIZED ARITHMETIC USED IN GARBAGE POWER GENERATION PLANTS ADDRESSING

摘要：

Neural network has ability of self-studying, self-adapting, fault tolerance and generalization. However, there are some defaults in its basic algorithm, such as low convergence speed, local extremes, and uncertain number of implied layer and implied notes. So there are some limitations in practice. In order to avoid these shortages, the paper solves these problems from two aspects. One is to adopt principle component analysis to select study samples and to make some of them containing more sample characteristics; the other is to train the network by using Levenberg-Marquardt backward propagation algorithm. Finally, an example is used to prove the new method is of high effectiveness and practicality in solving the addressing problem of garbage power generation plants.

关键词： Garbage power generation plant LM algorithm Neural network Location selection Principle component analysis

作者: YUAN-SHENG HUANG YAN ZHENG GANG LUO

作者单位: School of Business Administration, North China Electric Power University, Baoding 071003, China School of Business Administration, North China Electric Power University, Beijing 102206, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3148-3151

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）