Description

Complex Analysis and Neural Networks

Theory, Models & Applications

Complex Functions in Signal Processing, Stability of Networks

Artificial Intelligence has evolved far beyond conventional real-valued computation. Modern applications in signal processing, wireless communications, computer vision, speech recognition, radar systems, quantum computing, medical imaging, and deep learning increasingly rely on complex-valued mathematics for representing amplitude, phase, frequency, and multidimensional information.

Complex Analysis and Neural Networks: Theory, Models & Applications provides a rigorous yet practical introduction to the fascinating intersection of Complex Analysis, Signal Processing, and Artificial Intelligence.

The book begins with the mathematical foundations of complex numbers, complex functions, conformal mappings, analytic functions, Cauchy-Riemann equations, contour integration, Taylor and Laurent series, and residue calculus before extending these concepts into modern engineering applications.

Readers then explore the role of Fourier transforms, Laplace transforms, wavelet analysis, frequency-domain signal processing, and stability theory in designing robust intelligent systems. The final section introduces Complex-Valued Neural Networks (CVNNs), explaining how complex-valued representations improve learning efficiency, signal representation, optimization, convergence, and deep neural architectures.

Unlike traditional Complex Analysis textbooks that primarily focus on mathematical theory, this book connects advanced mathematics with modern AI applications including deep learning, speech processing, biomedical signal analysis, computer vision, wireless communication, radar signal processing, intelligent sensing, and complex-valued optimization.

Combining mathematical derivations, intuitive explanations, implementation concepts, engineering applications, and AI research perspectives, this book serves as a comprehensive resource for students, researchers, educators, and professionals working in Artificial Intelligence, Applied Mathematics, Signal Processing, and Computational Science.

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What You’ll Learn

✔ Complex Numbers

✔ Complex Plane

✔ Polar Representation

✔ Euler’s Formula

✔ Complex Algebra

✔ Complex Functions

✔ Analytic Functions

✔ Complex Mappings

✔ Conformal Mapping

✔ Bilinear Transformations

✔ Complex Differentiation

✔ Complex Integration

✔ Cauchy-Riemann Equations

✔ Harmonic Functions

✔ Laplace Equation

✔ Jacobian Matrix

✔ Taylor Series

✔ Laurent Series

✔ Singularities

✔ Residue Theorem

✔ Complex Fourier Series

✔ Fourier Transform

✔ Frequency Domain Analysis

✔ Signal Filtering

✔ Complex Convolution

✔ Audio Signal Processing

✔ Speech Processing

✔ Biomedical Signal Analysis

✔ Laplace Transform

✔ Region of Convergence

✔ Stability Analysis

✔ Transfer Functions

✔ Wavelet Transform

✔ Complex Morlet Wavelets

✔ Feature Extraction

✔ Time-Frequency Analysis

✔ Complex-Valued Neural Networks (CVNN)

✔ Complex Neurons

✔ Complex Activation Functions

✔ zReLU

✔ modReLU

✔ Split Activation Functions

✔ Wirtinger Calculus

✔ CR Calculus

✔ Complex Backpropagation

✔ Gradient Descent

✔ Complex CNN

✔ Complex RNN

✔ Complex LSTM

✔ Complex Transformers

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Table of Contents

Module I – Fundamentals of Complex Analysis

Chapter 1

Introduction to Complex Numbers

Chapter 2

Complex Functions & Mappings

Chapter 3

Analyticity and Cauchy–Riemann Equations

Chapter 4

Complex Differentiation & Integration

Chapter 5

Series & Residue Calculus

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Module II – Signal Processing Using Complex Functions

Chapter 6

Complex Fourier Transform & Applications

Chapter 7

Laplace Transform & Stability

Chapter 8

Wavelet Transform & Complex Wavelets

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Module III – Complex-Valued Neural Networks (CVNN)

Chapter 9

Introduction to Complex-Valued Neural Networks

Chapter 10

Complex Activation Functions

Chapter 11

Complex Backpropagation

Chapter 12

Architectures of Complex-Valued Neural Networks

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Who Should Read This Book?

This book is ideal for:

• B.Tech Students
• MCA Students
• M.Tech Students
• PhD Scholars
• Artificial Intelligence Researchers
• Machine Learning Engineers
• Deep Learning Engineers
• Signal Processing Engineers
• Communication Engineers
• Applied Mathematics Students
• Computer Science Students
• Data Scientists
• Robotics Engineers
• Computer Vision Researchers
• Biomedical Engineers
• Wireless Communication Engineers
• Research Scientists
• University Faculty
• AI Professionals
• Computational Mathematics Enthusiasts

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Key Features

✅ Comprehensive introduction to Complex Analysis for AI applications

✅ Mathematical foundations explained with engineering intuition

✅ Complex Analysis integrated with Neural Networks and Deep Learning

✅ Fourier, Laplace, and Wavelet Transform applications

✅ Complete introduction to Complex-Valued Neural Networks (CVNN)

✅ Stability analysis and optimization techniques

✅ Modern AI applications in signal processing and computer vision

✅ Research-oriented content with practical implementation concepts

✅ Suitable for university curricula and advanced research

✅ Bridges mathematics, signal processing, and Artificial Intelligence

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Why This Book?

Traditional Artificial Intelligence systems are primarily developed using real-valued mathematics, but many real-world applications naturally involve complex-valued signals where both magnitude and phase information are equally important. Signal processing, radar, telecommunications, speech recognition, medical imaging, quantum computing, and advanced neural networks all benefit from complex-domain computation.

This book provides a unified framework that connects Complex Analysis, Signal Processing, and Neural Networks, enabling readers to understand how advanced mathematical concepts directly improve intelligent learning systems.

Readers will gain both theoretical knowledge and practical insight into designing stable, efficient, and mathematically grounded AI models capable of processing complex-valued information.

Whether you are pursuing academic research, developing intelligent signal-processing systems, or exploring advanced deep learning architectures, this book offers a complete roadmap to mastering Complex Analysis for Artificial Intelligence.

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Book Details

Title: Complex Analysis and Neural Networks: Theory, Models & Applications

Subtitle: Complex Functions in Signal Processing, Stability of Networks

Author: Anshuman Mishra

Publisher: Anshuman Mishra

Publication Year: 2025

Language: English

Category: Artificial Intelligence, Complex Analysis, Neural Networks, Signal Processing, Applied Mathematics, Deep Learning