Titlebook: Methods and Applications of Intelligent Control; Spyros G. Tzafestas Book 1997 Springer Science+Business Media Dordrecht 1997 Fuzzy.Sensor

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Fuzzy and Neural Intelligent Control: Basic Principles and Architectures exact mathematical way. Throughout the years this theory was fully studied and used for the analysis, modeling and control of technological and nontechnological systems. Actually, our life and world obey the . of Zadeh, according to which “the closer one looks at a ‘real’ world problem, the fuzzier

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Intelligent Control Using Artificial Neural Networks and Fuzzy Logic: Recent Trends and Industrial Aperformance of the these systems has become increasingly evident. This may explain the dominant role of the emerging “intelligent systems” in recent years[.]. However, the definition of intelligent systems is a function of expectations and the status of the present knowledge: Perhaps the “intelligen

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Control of Robotic Manipulators Using Neural Networks — A Surveygorithms are capable of learning to control an unknown plant by extracting the necessary information from the plant. The purpose of this chapter is to provide an overview of the research being done in the area of neural network approaches to control of robotic manipulators. Applications of some neur

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Intelligent Process Control with Supervisory Knowledge-Based Systemsal controller designs are typically model based and a design that delivered excellent performance under nominal conditions may produce poor results for nonlinear systems when operating conditions are varied. Changes in operating conditions may occur through a setpoint change or nonstation-ary distur

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Fuzzy Model Based Predictive Controller develop a fuzzy controller which makes explicit use of the system dynamics expressed in terms of a set of rules. The fuzzy process modeling is accomplished by an inductive learning algorithm that is based on the concept of rough sets. The fuzzy process controller is constructed from the inverse fuz

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Fuzzy Adaptive Control Versus Model Reference Adaptive Control of Mutable Processesly necessary to obtain a high-performance control system. Many solutions have been proposed in order to make control systems adaptive. One of those solutions, model-reference adaptive system, evolved in the late 50s. The main innovation of this system is the presence of a reference model which speci

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Intelligent Neurofuzzy Estimators and Multisensor Data Fusion, is of prime importance in the monitoring and control of complex systems. This paper addresses the basic subproblems of MSDF within a unified informational framework derived via Neurofuzzy modelling and estimation algorithms. This environment provides a common framework for integrating information

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Intelligent Human — Machine Systems technical systems such as power plants, industrial production plants, and vehicles and transportation systems. High levels of safety, performance, and efficiency have been achieved by means of the increased use of automatic control.