结合Z-score与优化技术的神经模糊系统  

Neural Fuzzy System Combining Z-score and Optimize Techniques

作　　者：杜宏庆 陈德旺 DU Hong-qing;CHEN De-wang(College of Computer and Data Science/College of Software,Fuzhou University;Key Laboratory of Intelligent Metro of Univer-sity in Fujian,Fuzhou 350108,China)

机构地区：[1]福州大学计算机与大数据学院/软件学院,福建福州350108 [2]福州大学智慧地铁福建省高校重点实验室,福建福州350108

出　　处：《软件导刊》2022年第6期19-24,共6页Software Guide

基　　金：国家自然科学基金项目(61976055);福建省财政厅教育科研专项基金项目(GY-Z21001)。

摘　　要：在构建高维数据的深度神经模糊系统时,对模糊子模块的精度、时间的要求较高。为此,提出一种结合Zscore与优化技术的神经模糊系统,改进模糊规则的计算方式。根据不同方法调整前提参数与结论参数,提出基于BP+LSE的ZONFS1、基于LSE的ZONFS2、基于CGD+LSE的ZONFS3三种混合算法。实验结果表明,相较于ANFIS和ZONFS1算法,ZONFS3算法的耗时缩短了37%,且精度比DTR等算法提升了26%;相较于ZONFS3算法,ZONFS2的耗时减少,但精度降低;ZONFSi算法的平均总得分比ANFIS约高10分;相较于ANFIS算法,ZONFSi算法精度更高、耗时更少,在构建深度模型和处理高维数据方面优势显著。When constructing the deep neuro fuzzy system with high-dimensional data,the requirements for the accuracy and time of the fuzzy sub module are high.Therefore,a neural fuzzy system combining Z-score and optimization technology is proposed to improve the calculation method of fuzzy rules.According to different methods to adjust the premise parameters and conclusion parameters,three hybrid algorithms are proposed:ZONFS1 based on BP+LSE,ZONFS2 based on LSE and ZONFS3 based on CGD+LSE.The experimental results show that compared with ANFIS and ZONFS1 algorithms,the time consumption of ZONFS3 algorithm is reduced by 37%,and the accuracy is improved by 26%compared with DTR and other algorithms;Compared with ZONFS3 algorithm,ZONFS2 has less time-consuming but lower accuracy;The average total score of ZONFSi algorithm is about 10 points higher than ANFIS;Compared with ANFIS algorithm,ZONFSi algorithm has higher accuracy and less time consumption,and has significant advantages in building depth model and processing high-dimensional data.

关 键 词：神经模糊系统 参数优化 模糊规则 Z-SCORE CGD 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]