The development and application of neurofuzzy systems

Abstract

Fuzzy and neurofuzzy systems have been widely applied in the domestic products during the past 8 years for a number of reasons. In 1989, a large, 5 year fuzzy logic research and applications program was funded in Japan by MITI which involved over 45 industrial companies including most of the major automotive and electrical manufacturers. One of the main topics of this research was finding ways to make machines smarter by increasing the number of sensors and developing new ways to make use of this extra information. Sometimes the sensor only existed in algorithmic form (a soft sensor), where other measurements were combined to infer the value of a particular variable. Products which involved fuzzy systems therefore, generally either performed better or were easier to use, and as such were readily accepted by consumers in the Far East. Factors, such as a reduced product development times and a flexible framework, were cited as reasons why fuzzy techniques were a good solution, but these must be balanced against the fact that it would take just as long to validate the final solution and that the fuzzy solution was often the only system developed. The interest in fuzzy systems which occurred in Europe and America during the Nineties was partially in response to the wave of electrical consumer applications which appeared in the Far East. The first fuzzy control system was developed in the UK over 20 years ago, and during the Seventies, Mamdani and hi co-workers at Queen Mary College, London, developed a number of static and adaptive fuzzy PID-type controllers. This work involved testing their performance against standard PID controllers on a range of different plants. However, the first real applications of fuzzy control were developed in Holland in 1979 and was used to control a cement kiln. Peter Holmbald had attended a session where Mamdani presented a paper on self-organising controllers which concluded at the end that self-organising controllers were complicated and that he had recently stumbled on a better method: fuzzy logic. Fuzzy logic was therefore proposed as a method for simplifying the controller design process, making it: quicker to design, perform better than standard linear methods and less reliant on an accurate mathematical model of the plant. Mamdani's work was transferred to Japan after Prof Sugeno spent a six month sabbatical period working at Queen Mary College in 1982, and the fuzzy techniques began to be exploited after the successful development and deployment of the Sendai subway system by Hitachi in 1987, and a series of small laboratory experiments, such as the inverted pendulum, which demonstrated that fuzzy logic could be used to design and develop a wide variety of different control schemes. This paper will discuss how fuzzy logic has been used to improve controller design, and how neurofuzzy systems can be used to combine both symbolic (expert) and numerical (data) information to possibly overcome some of the weaknesses of each design paradigm.