深度神经模糊系统算法及其回归应用  

Deep Neural Fuzzy System Algorithm and Its Regression Application

作　　者：赵文迪 陈德旺 卓永强 黄允浒 ZHAO Wen-Di;CHEN De-Wang;ZHUO Yong-Qiang;HUANG Yun-Hu(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108;Key laboratory of Intelligent Metro of Universities in Fujian Province,Fuzhou University,Fuzhou 350108;College of Maritime,Guangzhou Maritime University,Guangzhou 510725)

机构地区：[1]福州大学数学与计算机科学学院,福州350108 [2]福州大学智慧地铁福建省高校重点实验室,福州350108 [3]广州航海学院海运学院,广州510725

出　　处：《自动化学报》2020年第11期2350-2358,共9页Acta Automatica Sinica

基　　金：国家自然科学基金面上项目(61976055);智慧地铁福建省高校重点实验室(53001703,50013203)资助。

摘　　要：深度神经网络是人工智能的热点,可以很好处理高维大数据,却有可解释性差的不足.通过IF-THEN规则构建的模糊系统,具有可解释性强的优点,但在处理高维大数据时会遇到“维数灾难”问题.本文提出一种基于ANFIS(Adaptive network based fuzzy inference system)的深度神经模糊系统(Deep neural fuzzy system,DNFS)及两种基于分块和分层的启发式实现算法:DNFS1和DNFS2.通过四个面向回归应用的数据集的测试,我们发现:1)采用分块、分层学习的DNFS在准确度与可解释性上优于BP、RBF、GRNN等传统浅层神经网络算法,也优于LSTM和DBN等深度神经网络算法;2)在低维问题中,DNFS1具有一定优势;3)在面对高维问题时,DNFS2表现更为突出.本文的研究结果表明DNFS是一种新型深度学习方法,不仅可解释性好,而且能有效解决处理高维数据时模糊规则数目爆炸的问题,具有很好的发展前景.Deep neural network is a hot spot of artificial intelligence,which can deal with high-dimensional big data well,but has the disadvantage of poor interpretability.The fuzzy system constructed by if-then rules has the advantage of strong interpretability,but it will encounter the problem of“the curse of dimension”when dealing with high dimension big data.This paper presents a DNFS(Deep neural fuzzy system)based on ANFIS(Adaptive network based fuzzy inference system)and two heuristic algorithms based on block and layer:DNFS1 and DNFS2.Through the testing of four regression-oriented data sets,we found:1)DNFS with block and layer learning is superior to BP,RBF,GRNN and other traditional shallow neural network algorithms in accuracy and interpretability,as well as LSTM,DBN and other deep neural network algorithms;2)In low dimensional problems,DNFS1 has certain advantages;3)In the face of high dimensional problems,DNFS2 is more prominent.The results of this paper show that DNFS is a new deep learning method,which not only has good interpretability,but also can effectively solve the problem that the number of fuzzy rules explodes when dealing with high-dimensional data,and has a good development prospect.

关 键 词：高维大数据 深度神经模糊系统 自适应神经模糊系统 分层结构 可解释性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]