Handbook of Neural Network Signal Processing

Yu Hen Hu, Jenq-Neng Hwang

• Publisher: CRC Press
• Year: 2018
• eBook ISBN: 9781351836302
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Biotechnology in Medicine
• Index: Technology & Engineering

The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

1

Introduction to Neural Networks for Signal Processing

Yu Hen Hu

University of Wisconsin

Jenq-Neng Hwang

University of Washington

1.1 Introduction

1.2 Artificial Neural Network (ANN) Models — An Overview

Basic Neural Network Components • Multilayer Perceptron (MLP) Model • Radial Basis Networks • Competitive Learning Networks • Committee Machines • Support Vector Machines (SVMs)

1.3 Neural Network Solutions to Signal Processing Problems

Digital Signal Processing

1.4 Overview of the Handbook

References

1.1 Introduction

The theory and design of artificial neural networks have advanced significantly during the past 20 years. Much of that progress has a direct bearing on signal processing. In particular, the nonlinear nature of neural networks, the ability of neural networks to learn from their environments in supervised as well as unsupervised ways, as well as the universal approximation property of neural networks make them highly suited for solving difficult signal processing problems.

From a signal processing perspective, it is imperative to develop a proper understanding of basic neural network structures and how they impact signal processing algorithms and applications. A challenge in surveying the field of neural network paradigms is to identify those neural network structures that have been successfully applied to solve real world problems from those that are still under development or have difficulty scaling up to solve realistic problems. When dealing with signal processing applications, it is critical to understand the nature of the problem formulation so that the most appropriate neural network paradigm can be applied. In addition, it is also important to assess the impact of neural networks on the performance, robustness, and cost-effectiveness of signal processing systems and develop methodologies for integrating neural networks with other signal processing algorithms. Another important issue is how to evaluate neural network paradigms, learning algorithms, and neural network structures and identify those that do and do not work reliably for solving signal processing problems.

This chapter provides an overview of the topic of this handbook — neural networks for signal processing. The chapter first discusses the definition of a neural network for signal processing and why it is important. It then surveys several modern neural network models that have found successful signal processing applications. Examples are cited relating to how to apply these nonlinear computation paradigms to solve signal processing problems. Finally, this chapter highlights the remaining contents of this book.

1.2 Artificial Neural Network (ANN) Models — An Overview

1.2.1 Basic Neural Ne...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• 1 Introduction to Neural Networks for Signal Processing
• 2 Signal Processing Using the Multilayer Perceptron
• 3 Radial Basis Functions
• 4 An Introduction to Kernel-Based Learning Algorithms
• 5 Committee Machines
• 6 Dynamic Neural Networks and Optimal Signal Processing
• 7 Blind Signal Separation and Blind Deconvolution
• 8 Neural Networks and Principal Component Analysis
• 9 Applications of Artificial Neural Networks to Time Series Prediction
• 10 Applications of Artificial Neural Networks (ANNs) to Speech Processing
• 11 Learning and Adaptive Characterization of Visual Contents in Image Retrieval Systems
• 12 Applications of Neural Networks to Image Processing
• 13 Hierarchical Fuzzy Neural Networks for Pattern Classification
• Index