Complex, Hypercomplex and Fuzzy-Valued Neural Networks : New Perspectives and Applications

Niemczynowicz, Agnieszka/ Perfilieva, Irina/ García-Raffi, Lluís M.

Routledge（2025/11発売）

Routledge（2025/11発売）

• 製本 Hardcover:ハードカバー版／ページ数 168 p.
• 言語 ENG
• 商品コード 9781032847146
• DDC分類 006.32

Full Description

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.

Contents

1. Preface 2. Introduction 3. Part I. Real-valued neural networks a. Applications in LLM models and RAG method b. Applications in image processing c. Application in time series analysis References 4. Part II. Complex- and Quaternionic-valued neural networks and their applications a. Applications in image processing b. Applications in time series analysis References 5. Part III. Theoretical Foundation of Computation with Neural Networks, from classic to fuzzy References 6. Conclusions References