Complex, Hypercomplex and Fuzzy-Valued Neural Networks : New Perspectives and Applications
Book Details
Format
Hardback or Cased Book
ISBN-10
103284714X
ISBN-13
9781032847146
Publisher
Taylor & Francis Ltd
Imprint
Routledge
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Nov 17th, 2025
Print length
168 Pages
Weight
342 grams
Dimensions
14.50 x 22.30 x 1.60 cms
Product Classification:
AlgebraProbability & statisticsProbability and statisticsApplied mathematicsAutomatic control engineeringMathematical theory of computationComputer architecture & logic designComputer architecture and logic designNeural networks & fuzzy systemsNeural networks and fuzzy systems
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This book explores the evolving landscape of neural network research, introducing readers to innovative mathematical approaches that extend beyond standard real-valued models.
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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