Fuzzy Logic Augmentation of Neural and Optimization Algorithms : Theoretical Aspects and Real Applications / edited by Oscar Castillo, Patricia Melin, Janusz Kacprzyk.
Castillo, Oscar. editor.; Melin, Patricia. editor.; Kacprzyk, Janusz. editor.
2018
Q342
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Title Fuzzy Logic Augmentation of Neural and Optimization Algorithms : Theoretical Aspects and Real Applications / edited by Oscar Castillo, Patricia Melin, Janusz Kacprzyk.
ISBN 9783319710082
3319710087
9783319710075
DOI https://doi.org/10.1007/978-3-319-71008-2
Published Cham : Springer International Publishing : Imprint: Springer, 2018.
Language English
Description 1 online resource (xi, 546 pages) : illustrations.
Item Number 10.1007/978-3-319-71008-2 doi
Call Number Q342
Dewey Decimal Classification 511.3/13
Summary This book comprises papers on diverse aspects of fuzzy logic, neural networks, and nature-inspired optimization meta-heuristics and their application in various areas such as intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction and optimization of complex problems. The book is organized into seven main parts, each with a collection of papers on a similar subject. The first part presents new concepts and algorithms based on type-2 fuzzy logic for dynamic parameter adaptation in meta-heuristics. The second part discusses network theory and applications, and includes papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The third part addresses the theory and practice of meta-heuristics in different areas of application, while the fourth part describes diverse fuzzy logic applications in the control area, which can be considered as intelligent controllers. The next two parts explore applications in areas, such as time series prediction, and pattern recognition and new optimization and evolutionary algorithms and their applications respectively. Lastly, the seventh part addresses the design and application of different hybrid intelligent systems.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Added Author Castillo, Oscar. editor.
Melin, Patricia. editor.
Kacprzyk, Janusz. editor.
Series Studies in computational intelligence ; 749.
Available in Other Form Print version: 9783319710075
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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.

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