The direct adaptive regulation of nonlinear dynamical systems in Brunovsky form with modeling error effects, is considered in this paper. The method is based on a new Neuro-Fuzzy Dynamical System definition, which uses the concept of Fuzzy Adaptive Systems (FAS) operating in conjunction with High Order Neural Network Functions (HONNFs). Since the plant is considered unknown, we first propose it...

Nature-inspired methodologies are currently among the most powerful algorithms for optimization problems. This paper presents a recent nature-inspired algorithm named Firefly algorithm (FA) for automatically evolving a fuzzy model from numerical data. FA is a meta-heuristic inspired by the flashing behavior of fireflies. The rate and the rhythmic flash, and the amount of time form part of the s...

image segmentation is an essential issue in image description and classification. currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. moreover, there are many uncertainties and vagueness in images, which crisp clustering and even type-1 fuzzy clustering could not handle. hence, type-...

This paper summarizes work undertaken in the area of modeling Shape Memory Alloy (SMA) and airfoil hysteresis using a Sugeno-type fuzzy modeling approach based on subtractive clustering. Two alternative approaches to develop a fuzzy model for hysteresis are proposed and evaluated. The first consists in building a mirror image of the lower curve in order to model both curves concurrently and the...

In general, the first and the most important step in system analysis, prediction and control is the proper model of the system. In order to design the controller of nonlinear electrohydraulic system, several modeling techniques are proposed: the transfer function of the electrohydraulic system is identified using first-principle method, and the intelligent models are built by fuzzy modeling and...

This paper describes a novel nonlinear modeling approach by on-line clustering, fuzzy rules and support vector machine. Structure identification is realized by an on-line clustering method and fuzzy support vector machines, the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upper bounds of t...

In order to build accurate model for complicated nonlinear system in engineering, like boiler–turbine system, a novel fuzzy-modeling approach is proposed, which is based on a new fuzzy c-regression model (NFCRM) clustering algorithm and is able to determine the right number of rules automatically. In this method, NFCRM is applied to build the fuzzy structure and then identify the premise parame...

generalized geraghty type fuzzy mappings oncomplete metric spaces are introduced and a fixed point theorem thatgeneralizes some recent comparable results for fuzzy mappings incontemporary literature is obtained. example is provided to show thevalidity of obtained results over comparable classical results for fuzzymappings in fixed point theory. as an application, existence of coincidencefuzzy p...