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Complex, Hypercomplex and Fuzzy-Valued Neural Networks
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Complex, Hypercomplex, and Fuzzy-Valued Neural Networks are extensions of classical neural networks to higher dimensions. In recent decades, this theory has emerged as a forefront in neural networks theory. There are several approaches to extend classical neural network models: quaternionic analysis, which merely uses quaternions; Clifford analysis, which relies on Clifford algebras; and finally generalizations of complex variables to higher dimensions. This book reflects a selection of papers related to complex, hypercomplex analysis, and fuzzy approaches applied to neural networks theory. The topics covered represent new perspectives and current trends in neural networks and their applications to mathematical physics, image analysis and processing, mechanics, and beyond.
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Keywords
- advanced neural network architectures
- Cayley-Dickson
- Clifford algebra
- complex-valued
- computational mechanics
- data analysis
- fuzzy logic systems
- hypercomplex-valued
- image processing techniques
- mathematical physics applications
- neural network theory
- quaternion
- thema EDItEUR::P Mathematics and Science::PB Mathematics::PBF Algebra
- thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
- thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYF Computer architecture and logic design
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems
- time series modeling
Links
DOI: 10.1201/9781003515302Editions