<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/5332" mdate="2007-09-20 00:00:00"> <author>Seema Chopra and R. Mitra and Vijay Kumar</author> <title>Neural Network Tuned Fuzzy Controller for MIMO System</title> <pages>1320 - 1327</pages> <year>2007</year> <volume>1</volume> <number>9</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/5332</ee> <url>https://publications.waset.org/vol/9</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>In this paper, a neural network tuned fuzzy controller is proposed for controlling MultiInput MultiOutput (MIMO) systems. For the convenience of analysis, the structure of MIMO fuzzy controller is divided into single input singleoutput (SISO) controllers for controlling each degree of freedom. Secondly, according to the characteristics of the systems dynamics coupling, an appropriate coupling fuzzy controller is incorporated to improve the performance. The simulation analysis on a twolevel mass&amp;ndash;spring MIMO vibration system is carried out and results show the effectiveness of the proposed fuzzy controller. The performance though improved, the computational time and memory used is comparatively higher, because it has four fuzzy reasoning blocks and number may increase in case of other MIMO system. Then a fuzzy neural network is designed from a set of inputoutput training data to reduce the computing burden during implementation. This control strategy can not only simplify the implementation problem of fuzzy control, but also reduce computational time and consume less memory.</abstract> <index>Open Science Index 9, 2007</index> </article>