Complex, Hypercomplex and Fuzzy-Valued Neural Networks: New Perspectives and Applications
暫譯: 複雜、超複雜及模糊值神經網絡：新視角與應用

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

Complex, Hypercomplex and Fuzzy-Valued Neural Networks: New Perspectives and Applications
  • 出版商: Routledge
  • 出版日期: 2025-11-17
  • 售價: $2,910
  • 貴賓價: 9.5 折 $2,765
  • 語言: 英文
  • 頁數: 168
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 103284714X
  • ISBN-13: 9781032847146
  • 相關分類: DeepLearning

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相關主題

商品描述

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.

商品描述(中文翻譯)

複數、超複數與模糊值神經網路是經典神經網路在更高維度上的擴展。在近幾十年中，這一理論已成為神經網路理論的前沿。擴展經典神經網路模型有幾種方法：四元數分析，僅使用四元數；克利福德分析，依賴於克利福德代數；以及將複變數推廣到更高維度的概念。本書反映了一系列與複數、超複數分析及應用於神經網路理論的模糊方法相關的論文。所涵蓋的主題代表了神經網路及其在數學物理、影像分析與處理、力學等領域應用的新視角和當前趨勢。

作者簡介

Agnieszka Niemczynowicz, PhD, is an Associate Professor at Cracow University of Technology. Her work focuses on mathematical modeling, data analysis, and machine learning, applied across science and engineering. She has published 50 articles, led international grants, and received the 2022 Doak Award for a top paper in the Journal of Sound and Vibration.racow University of Technology, Poland

Irina Perfilieva, Ph.D., Dr.h.c., is an author and co-author of seven books on mathematical principles of fuzzy sets and fuzzy logic, and more than 270 papers in the area of fuzzy logic, fuzzy approximation and fuzzy relation equations. She has received several awards, including an IFSA fellow and an honorary member of EUSFLAT. Her recent interests are in the area of data analysis and the mathematical foundation of neural networks.

Dr. Luis M. Garcia Raffi is a full professor in Applied Mathematics at Universitat Politècnica de València, with PhDs in Physics and Mathematics. His research spans Physics (Nuclear Physics, Phononics), Mathematics (Analysis, Topology, Machine Learning), and Didactics. He has authored several articles, collaborated internationally, and teaches AI-related topics.

Radoslaw Antoni Kycia holds PhDs in Physics (Jagiellonian University) and Geometry, Topology and Geometric Analysis (Masaryk University). He is an Associate Professor at Cracow University of Technology. His research focuses on quantum systems, topology, and machine learning. He has published over 40 articles and participated in national and EU-funded scientific projects.

作者簡介(中文翻譯)

Agnieszka Niemczynowicz 博士是克拉科夫科技大學的副教授。她的研究專注於數學建模、數據分析和機器學習，應用於科學和工程領域。她已發表50篇文章，主導國際研究計畫，並因在《聲音與振動期刊》上發表的優秀論文獲得2022年Doak獎。

Irina Perfilieva 博士，榮譽博士，是七本有關模糊集合和模糊邏輯數學原則的書籍的作者和合著者，以及270多篇有關模糊邏輯、模糊近似和模糊關係方程的論文的作者。她獲得了多項獎項，包括IFSA研究員和EUSFLAT榮譽會員。她最近的研究興趣集中在數據分析和神經網絡的數學基礎領域。

Dr. Luis M. Garcia Raffi 是瓦倫西亞理工大學應用數學的全職教授，擁有物理學和數學的博士學位。他的研究涵蓋物理學（核物理學、聲子學）、數學（分析、拓撲、機器學習）和教學法。他已發表多篇文章，並進行國際合作，教授與人工智慧相關的主題。

Radoslaw Antoni Kycia 擁有物理學（雅蓋隆大學）和幾何學、拓撲學及幾何分析（馬薩里克大學）的博士學位。他是克拉科夫科技大學的副教授。其研究專注於量子系統、拓撲學和機器學習。他已發表超過40篇文章，並參與國家及歐盟資助的科學計畫。

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