Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]
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Keywords:
Soft Computing
Soft Computing
Natural computing
Swarm intelligence
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In recent times, engineers have very well accepted soft computing techniques such as fuzzy sets theory, neural nets, neuro fuzzy system, adaptive neuro fuzzy inference system (ANFIS), coactive neuro fuzzy inference system (CANFIS), evolutionary computing, probabilistic computing, Computational intelligence (CI), etc. for carrying out varying numerical simulation analysis. In last two decades, these techniques have been successfully applied in various engineering problems independently or in hybrid forms. The main objective of this paper is to introduce engineers and students about the latest trends in soft computing. Also they will help young researchers to develop themselves in futures.In recent years Computational intelligence (CI) has gained a widespread concern of many scholars emerging as a new field of study. CI actually uses the bionics ideas for reference, it origins from emulating intelligent phenomenon in nature. CI attempts to simulate and reappearance the characters of intelligence, such as learning and adaptation, so that it can be a new research domain for reconstructing the nature and engineering. The essence of CI is a universal approximator, and it has the great function of non-linear mapping and optimization.In this paper we give an overview of intelligent systems. We discuss the notion itself, together with diverse features and constituents of it. We concentrate especially on computational intelligence and soft computing.
Soft Computing
Bionics
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Soft Computing
St petersburg
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This paper describes DANIELA a neuro-fuzzy system for control applications. The system is based on a custom neural device that can implement either multilayer perceptrons, radial basis functions or fuzzy paradigms. The system implements intelligent control algorithms mixing neuro-fuzzy paradigms with finite state automata and is used to control a walking hexapod.
Hexapod
Perceptron
Intelligent Control
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Computational Intelligence or Soft Computing methods refer to three concepts viz. Artificial Neural Network, Fuzzy Systems and Evolutionary Algorithm (EA) or Genetic Algorithm (GA). The Computational Intelligence methodologies facilitate a better data acquisition from our everyday environment, and thus help in extending the capabilities of conventional computer systems.
Soft Computing
Realization (probability)
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This work shows a stand-alone photovoltaic system application based on fuzzy logic controllers and genetic fuzzy systems. A hierarchical fuzzy controller has been designed that at present controls four real stand-alone photovoltaic systems sited in University of Jaen. In order to improve that fuzzy logic controller operation, a genetic fuzzy system has been designed. Its operation is based on a mathematical model obtained from the system to be controlled. This work main objective consist on the verification that the individuals, which have been generated using the genetic fuzzy system, properly control the photovoltaic real installations.
Fuzzy electronics
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We comment upon the very essence, roots, potentials, and applicability of computational intelligence and soft computing. We followed a different path than those traditionally employed, and which are so well and in a deep and comprehensive way documented in other papers in this special issue. First, we consider relations between computational intelligence and artificial intelligence, starting from a reference to different kinds of views if how intelligence is meant in science, and whether it has a general nature or many different types. Then, we consider the issue of symbolic and numerical calculations, and the two attitudes for problem solving: a general one and problem specific. Then, we discuss soft computing – which is in our view a narrower area than computational intelligence – and consider two aspects implied by the inclusion of the two words: “computing” and “soft”. First, we discuss how soft computing relates to the traditionally meant computing [in the sense of (a theory of) computation]. Second, we pursue a novel path that has not been practically considered in the literature, that is – first – relations of soft computing to soft sciences, and – second – relations of soft computing to Checkland's soft systems methodology. We hope that this paper will trigger a discussion and stimulate a new line of research with both soft computing and computational intelligence communities, on the one hand, and a broadly perceived soft science communities, on the other hand, that can bring about new ideas and novel problem formulations and solutions.
Soft Computing
Artificial General Intelligence
Soft Systems Methodology
Theory of computation
Symbolic artificial intelligence
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Soft Computing
Component (thermodynamics)
Ambient Intelligence
Autonomic Computing
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Neuro-fuzzy approach for implementing control systems is considered. Neuro-fuzzy systems are a tool for a development of trainable control systems with high interpretability. These systems can be trained to work in new conditions. There is a possibility to analyze the actions, which implement the control. Examples of neuro-fuzzy control applications are presented: virtual assistant and automatic calibration system.
Interpretability
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