Abstract
Fuzzy logic has created a high impact on research and development in almost all engineering applications. Recently, there has been an increasing interest in various offshoots of fuzzy logic approach and hierarchical fuzzy logic is one such area of research development and applications. With the increase in volume of data, hierarchical fuzzy logic has emerged as a highly suitable candidate for research. The objective of this paper is to develop methodology for multi-input multi-output hierarchical fuzzy systems. In particular, a system to be designed is broken into a number of sub-subsystems, where each subsystem is designed separately and then connected in hierarchical structure. The strategy used in this paper is to avoid the repetition of common terms across different subsystems of multi-input multi-output systems. This strategy has not been presented hitherto by any other author. This paper first discusses in detail the implementation of a multi-input single-output hierarchical system. It then extends the approach to multi-input multi-output hierarchical systems.
Highlights
In the year 1965, Lotfi Zadeh first introduced the term ‘‘Fuzzy Logic’’ in his research paper on fuzzy sets [1]
Fuzzy logic is an effective means to resolve conflicts and provide realistic assessments because of its ability to deal with information that is uncertain, imprecise, or vague etc
Since the advent of classic papers by Lotfi Zadeh [1], fuzzy logic has been used for a large number of applications in different disciplines and used in real life applications [6]
Summary
In the year 1965, Lotfi Zadeh first introduced the term ‘‘Fuzzy Logic’’ in his research paper on fuzzy sets [1]. The main advantage of neuro-fuzzy systems is its ability to act as a feed-forward network, commonly known as universal approximation and interpret IF- rules Both Mamdani and Sugeno models [10] have given a boost to the work suggested by Lotfi Zadeh [1]. Increase in input variables lead to increase in rules, increasing the overall complexity of the system This limitation restricts the usage of fuzzy systems to solve complex problems and real-life applications with large dimensions. This paper presents an approach to develop the hierarchical fuzzy based model with the focus to deal with large rule dimensions and especially in multi-output environments, without compromising the performance and effectiveness of the overall system.
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