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Introduction to Fuzzy Logic using MATLAB
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Fuzzy Set Theory―and Its Applications
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Bandit Algorithms
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From T2 FS-based MoGoTW System to DyNaDF for Human and Machine Co-Learning on Go.- Ordered Novel Weighted Averages.- On The Comparison of Model-Based and Model-Free Controllers in Guidance, Navigation and Control of Agricultural Vehicles.- Important and Challenging Issues for Interval Type-2 Fuzzy Control Research.- Type-2 Fuzzy Logic in pattern recognition applications.- Type-2 Fuzzy Logic Control in Computer Games.- A Type-2 Fuzzy Model to Prioritize Suppliers Based on Trust Criteria in Intelligent Agent-Based Systems. • ISBN:9783319728919 • Format:Hardcover • Publication Date:2018-02-15
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This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of pattern recognition systems, to achieve the ultimate goal of pattern recognition. This book also shows results of the application of hybrid intelligent systems to real-world problems of face, fingerprint, and voice recognition. This monograph is intended to be a major reference for scientists and engineers applying new computational and mathematical tools to intelligent pattern recognition and can be also used as a textbook for graduate courses in soft computing, intelligent pattern recognition, computer vision, or applied artificial intelligence.Product DetailsISBN-13: 9783642063251 Publisher: Springer Berlin Heidelberg Publication Date: 11-25-2010 Pages: 272 Product Dimensions: 6.10(w) x 9.25(h) x 0.02(d) Series: Studies in Fuzziness and Soft Computing #172
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The past fifteen years has witnessed an explosive growth in the fundamental research and applications of artificial neural networks (ANNs) and fuzzy logic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern recognition, prediction and control. Although the origins of ANNs and FL may be traced back to the 1940s and 1960s, respectively, the most rapid progress has only been achieved in the last fifteen years. This has been due to significant theoretical advances in our understanding of ANNs and FL, complemented by major technological developments in high-speed computing. In geophysics, ANNs and FL have enjoyed significant success and are now employed routinely in the following areas (amongst others): 1. Exploration Seismology. (a) Seismic data processing (trace editing; first break picking; deconvolution and multiple suppression; wavelet estimation; velocity analysis; noise identification/reduction; statics analysis; dataset matching/prediction, attenuation), (b) AVO analysis, (c) Chimneys, (d) Compression I dimensionality reduction, (e) Shear-wave analysis, (f) Interpretation (event tracking; lithology prediction and well-log analysis; prospect appraisal; hydrocarbon prediction; inversion; reservoir characterisation; quality assessment; tomography). 2. Earthquake Seismology and Subterranean Nuclear Explosions. 3. Mineral Exploration. 4. Electromagnetic I Potential Field Exploration. (a) Electromagnetic methods, (b) Potential field methods, (c) Ground penetrating radar, (d) Remote sensing, (e) inversion. Modern Approaches in Geophysics: Geophysical Applications of Artifici
al Neural Networks and Fuzzy Logic (Paperback)
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From T2 FS-based MoGoTW System to DyNaDF for Human and Machine Co-Learning on Go.- Ordered Novel Weighted Averages.- On The Comparison of Model-Based and Model-Free Controllers in Guidance, Navigation and Control of Agricultural Vehicles.- Important and Challenging Issues for Interval Type-2 Fuzzy Control Research.- Type-2 Fuzzy Logic in pattern recognition applications.- Type-2 Fuzzy Logic Control in Computer Games.- A Type-2 Fuzzy Model to Prioritize Suppliers Based on Trust Criteria in Intelligent Agent-Based Systems. • ISBN:9783319892184 • Format:Paperback • Publication Date:2019-06-04
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This book covers hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Includes basic theory, use of type-2 fuzzy models, optimization of type-2 fuzzy systems and modular neural networks and more. Part I: Basic Concepts and Theory.- Part II Modular Neural Networks in Pattern Recognition.- Part III Optimization of Modular Neural Networks for Pattern Recognition. • Author: Patricia Melin • ISBN:9783642270277 • Format:Paperback • Publication Date:2013-11-30
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This book covers hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Includes basic theory, use of type-2 fuzzy models, optimization of type-2 fuzzy systems and modular neural networks and more. Studies in Computational Intelligence: Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition (Hardcover)
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Part I: Type-2 Fuzzy Logic in Metaheuristics.- A comparative study of dynamic adaptation of parameters in the GWO algorithm using type-1 and interval type-2 fuzzy logic.- Ensemble Neural Network optimization using a gravitational search algorithm with Interval Type-1 and Type-2 fuzzy parameter adaptation in pattern recognition applications.- Improved method based on type-2 fuzzy logic for the adaptive harmony search algorithm.- Comparison of bio-inspired methods with parameter adaptation through interval type-2 fuzzy logic.- Differential Evolution algorithm with Interval type-2 fuzzy logic for the optimization of the mutation parameter.- Part II: Neural Networks Theory and Applications.- Person recognition with modular deep neural network using the iris biometric measure.- Neuro-evolutionary Neural Network for the Estimation of Melting Point of Ionic Liquids.- A proposal to classify ways of walking patterns using spik-ing neural networks.- Partially-connected Artificial Neural Networks developed by Grammatical Evolution for pattern recognition problems.- Part III: Metaheuristics: Theory and Applications.- Bio-inspired Metaheuristics for Hyper-parameter Tuning of Support Vector Machine Classifiers. • ISBN:9783319890289 • Format:Paperback • Publication Date:2019-06-06
