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Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.


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22 Jan 2003
TL;DR: This chapter discusses Fuzzy Logic, a model for controlling Chaos through Feedback, and its applications in Plant Monitoring and Diagnosis and Genetic Algorithms.
Abstract: 1 Introduction- 2 Type-1 Fuzzy Logic- 21 Type-1 Fuzzy Set Theory- 22 Fuzzy Rules and Fuzzy Reasoning- 221 Fuzzy Relations- 222 Fuzzy Rules- 23 Fuzzy Inference Systems- 24 Fuzzy Modelling- 25 Summary- 3 Type-2 Fuzzy Logic- 31 Type-2 Fuzzy Sets- 32 Operations of Type-2 Fuzzy Sets- 33 Type-2 Fuzzy Systems- 331 Singleton Type-2 Fuzzy Logic Systems- 332 Non-Singleton Fuzzy Logic Systems- 333 Sugeno Type-2 Fuzzy Systems- 34 Summary- 4 Supervised Learning Neural Networks- 41 Backpropagation for Feedforward Networks- 411 The Backpropagation Learning Algorithm- 412 Backpropagation Multilayer Perceptrons- 413 Methods for Speeding up Backpropagation- 42 Radial Basis Function Networks- 43 Adaptive Neuro-Fuzzy Inference Systems- 431 ANFIS Architecture- 432 Learning Algorithm- 44 Summary- 5 Unsupervised Learning Neural Networks- 51 Competitive Learning Networks- 52 Kohonen Self-Organizing Networks- 53 Learning Vector Quantization- 54 The Hopfield Network- 55 Summary- 6 Genetic Algorithms and Simulated Annealing- 61 Genetic Algorithms- 62 Modifications to Genetic Algorithms- 621 Chromosome Representation- 622 Objective Function and Fitness- 623 Selection Methods- 624 Genetic Operations- 625 Parallel Genetic Algorithm- 63 Simulated Annealing- 64 Applications of Genetic Algorithms- 641 Evolving Neural Networks- 6411 Evolving Weights in a Fixed Network- 6412 Evolving Network Architectures- 642 Evolving Fuzzy Systems- 65 Summary- 7 Dynamical Systems Theory- 71 Basic Concepts of Dynamical Systems- 72 Controlling Chaos- 721 Controlling Chaos through Feedback- 7211 Ott-Grebogi-Yorke Method- 7212 Pyragas's Control Methods- 7213 Controlling Chaos by Chaos- 722 Controlling Chaos without Feedback- 7221 Control through Operating Conditions- 7222 Control by System Design- 7223 Taming Chaos- 723 Method Selection- 73 Summary- 8 Plant Monitoring and Diagnostics- 81 Monitoring and Diagnosis- 82 Fractal Dimension of a Geometrical Object- 83 Fuzzy Estimation of the Fractal Dimension- 84 Plant Monitoring with Fuzzy-Fractal Approach- 85 Experimental Results- 86 Summary- 9 Adaptive Control of Non-Linear Plants- 91 Fundamental Adaptive Fuzzy Control Concept- 92 Basic Concepts of Stepping Motors- 921 Variable Reluctance Motors- 922 Unipolar Motors- 923 Bipolar Motors- 924 Dynamics of the Stepping Motor- 925 Control of the Stepping Motor- 93 Fuzzy Logic Controller of the Stepping Motor- 94 Hardware Implementation of ANFIS- 95 Experimental Results- 96 Summary- 10 Automated Quality Control in Sound Speaker Manufacturing- 101 Introduction- 102 Basic Concepts of Sound Speakers- 1021 Sound Basics- 1022 Making Sound- 1023 Chunks of the Frequency Range- 1024 Boxes of Sound- 1025 Alternative Speaker Designs- 103 Description of the Problem- 104 Fractal Dimension of a Sound Signal- 105 Experimental Results- 106 Summary- 11 Intelligent Manufacturing of Television Sets- 111 Introduction- 112 Imaging System of the Television Set- 1121 The Cathode Ray Tube- 1122 Phosphor- 1123 The Black-and-White TV Signal- 1124 Adding Color- 113 Breeder Genetic Algorithm for Optimization- 1131 Genetic Algorithm for Optimization- 114 Automated Electrical Tuning of Television Sets- 115 Intelligent System for Control- 116 Simulation Results- 117 Summary- 12 Intelligent Manufacturing of Batteries- 121 Intelligent Control of the Battery Charging Process- 1211 Problem Description- 1212 Fuzzy Method for Control- 1213 Neuro-Fuzzy Method for Control- 1214 Neuro-Fuzzy-Genetic Method for Control- 122 Hardware Implementation of the Fuzzy Controller for the Charging Process- 1221 Introduction- 1222 Fuzzy Control- 1223 Implementation of the Fuzzy Controller- 1224 Experimental Results- 123 Automated Quality Control of Batteries- 1231 Introduction- 1232 Fuzzy Controller- 1233 Fuzzy Control Implementation- 124 Summary

76 citations

Journal ArticleDOI

76 citations

Journal ArticleDOI
TL;DR: A new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot, is proposed.

75 citations

Journal ArticleDOI
TL;DR: The development of models using Artificial Neural Network with back propagation and Levenberg-Maquardt algorithms, radial basis function, Fuzzy Logic, and decision tree algorithms such as M5 and REPTree for predicting the suspended sediment concentration at Kasol, upstream of the Bhakra reservoir in northern India are presented.
Abstract: The prediction of the sediment loading generated within a watershed is an important input in the design and management of water resources projects. High variability of hydro-climatic factors with sediment generation makes the modelling of the sediment process cum- bersome and tedious. The methods for the estimation of sediment concentration based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment concentration. The focus of this paper is to present the development of models using Artificial Neural Network (ANN) with back propagation and Levenberg-Maquardt algorithms, radial basis function (RBF), Fuzzy Logic, and decision tree algorithms such as M5 and REPTree for predicting the suspended sediment concentration at Kasol, upstream of the Bhakra reservoir, located in the Sutlej basin in northern India. The input vector to the various models using different algorithms was derived con- sidering the statistical properties such as auto-correlation function, partial auto-correlation, and cross-correlation function of the time series. It was found that the M5 model performed well compared to other soft computing techniques such as ANN, fuzzy logic, radial basis function, and REPTree investigated in this study, and results of the M5 model indicate that all ranges of sediment concentration values were simulated fairly well. This study also suggests that M5 model trees, which are analogous to piecewise linear functions, have certain advantages over other soft computing techniques because they offer more insight into the generated model, are acceptable to decision makers, and always converge. Further, the M5 model tree offers explicit expressions for use by field engineers. DOI: 10.1061/(ASCE)HE.1943-5584.0000445. © 2012 American Society of Civil Engineers. CE Database subject headings: Suspended sediment; Neural networks; Fuzzy sets; Reservoirs. Author keywords: Suspended sediment concentration; Neural networks; Fuzzy Logic; M5; REPTree; Bhakra reservoir.

75 citations

Journal ArticleDOI
TL;DR: The proposed soft computing method can reliably estimate the PCI and can be used in a pavement management system (PMS) using simple and accessible spreadsheet softwares and showed that the ANN- and GP-based projected values are in good agreement with the field-measured data.
Abstract: The pavement condition index (PCI) is a widely used numerical index for the evaluation of the structural integrity and operational condition of pavements. Estimation of the PCI is based on the results of a visual inspection in which the type, severity, and quantity of distresses are identified. The purpose of this study is to develop an alternative approach for forecasting the PCI using optimization techniques, including artificial neural networks (ANN) and genetic programming (GP). The proposed soft computing method can reliably estimate the PCI and can be used in a pavement management system (PMS) using simple and accessible spreadsheet softwares. A database composed of the PCI results of more than 1,250 km of highways in Iran was used to develop the models. The results showed that the ANN- and GP-based projected values are in good agreement with the field-measured data. In addition, the ANN-based model was more precise than the GP-based model. For more straightforward applications, a computer program was developed based on the results obtained.

75 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023159
2022270
2021319
2020332
2019313
2018348