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Fuzzy associative matrix
About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.
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TL;DR: It is proved that if the derivative of Lyapunov function is negative semi-definite in the active region of each fuzzy rule, then the overall system will be asymptotically stable in the sense of LyAPunov (ISL).
Abstract: This paper presents a new stability analysis method for nonlinear processes with Takagi-Sugeno (T-S) fuzzy logic controllers (FLCs). The design of the FLCs is based on heuristic fuzzy rules. The stability analysis of these fuzzy control systems is performed using LaSalle's invariant set principle with non-quadratic Lyapunov candidate function. This paper proves that if the derivative of Lyapunov function is negative semi-definite in the active region of each fuzzy rule, then the overall system will be asymptotically stable in the sense of Lyapunov (ISL). The stability theorem suggested in the paper ensures sufficient stability conditions for fuzzy control systems controlling a class of nonlinear processes. The end of the paper contains an illustrative example that describes an application of the stability analysis method.
50 citations
TL;DR: A robust type of fuzzy clustering algorithm with a noise cluster is proposed that is robust with respect to noise and can also detect outliers for conical fuzzy vector data.
50 citations
TL;DR: The objective is to investigate and evaluate the proposed rule-based model against commonly used time series models including ''standard'' architectures such as autoregressive (AR) models and selected topologies of neural networks.
50 citations
26 Jun 1994
TL;DR: This paper investigates the design of a fuzzy logic PID controller that uses a simplified design scheme that takes advantage of the properties of the fuzzy PI and PD controllers.
Abstract: This paper investigates the design of a fuzzy logic PID controller that uses a simplified design scheme. Fuzzy logic PD and PI controllers are effective for many control problems but lack the advantages of the fuzzy PID controller. Fuzzy controllers use a rule base to describe relationships between the input variables. Implementation of a detailed rule base, such as a PID controller, increases in complexity as the number of input variables grow and the ranges of operation for the variables become more defined. We propose a hybrid fuzzy PID controller which takes advantage of the properties of the fuzzy PI and PD controllers. The effectiveness of the PID fuzzy controller implementation is illustrated with an example. >
50 citations
TL;DR: The consensus problem of multiagent nonlinear systems (MANNs) with variable structure is discussed and it is shown that the consensus of the global fuzzy model is equivalent to that of its corresponding small-scale fuzzy models.
Abstract: The consensus problem of multiagent nonlinear systems (MANNs) with variable structure is discussed in this paper. T-S fuzzy models are first presented to describe MANNs with variable structure. The nodes of each T-S fuzzy model are rearranged so that the global fuzzy model is decomposed into independent and small-scale fuzzy models. It is shown that the consensus of the global fuzzy model is equivalent to that of its corresponding small-scale fuzzy models in which the continuous and sampled controllers are applied. Sufficient conditions are derived to ensure the consensus of the controlled fuzzy models. Finally, simulation results are given to illustrate the effectiveness of the proposed criteria.
50 citations