Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Necessary conditions for some typical fuzzy systems as universal approximators
Automatica (Journal of IFAC)
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
Fault detection for fuzzy systems with intermittent measurements
IEEE Transactions on Fuzzy Systems
A new pattern of knowledge based on experimenting the causality relation
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems
Expert Systems with Applications: An International Journal
Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers
Expert Systems with Applications: An International Journal
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In this paper we present a new method of interval fuzzy model identification. The method combines a fuzzy identification methodology with some ideas from linear programming theory. On a finite set of measured data, an optimality criterion that minimizes the maximal estimation error between the data and the proposed fuzzy model output is used. The idea is then extended to modelling the optimal lower and upper bound functions that define the band that contains all the measurement values. This results in a lower and an upper fuzzy model or a fuzzy model with a set of lower and upper parameters. The model is called the interval fuzzy model (INFUMO). The method can be used when describing a family of uncertain nonlinear functions or when the systems with uncertain physical parameters are observed. We believe that the fuzzy interval model can be very efficiently used, especially in fault detection and in robust control design.