Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition, 1st Edition (Hardback) book cover

Neural Networks for Applied Sciences and Engineering

From Fundamentals to Complex Pattern Recognition, 1st Edition

By Sandhya Samarasinghe

Auerbach Publications

570 pages | 374 B/W Illus.

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pub: 2006-09-12
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Description

In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks.

Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis.

With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics.

Features

§ Explains neural networks in a multi-disciplinary context

§ Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding

? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting

§ Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters

Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA. Her neural networks research focuses on theoretical understanding and advancements as well as practical implementations.

Reviews

"Beginning with the basics, she [Samarasinghe] explains a variety of neural networks' internal workings, and how to apply them to solve real problems."

-SciTech Book News, December 2006

Table of Contents

FROM DATA TO MODELS: COMPLEXITY AND CHALLENGES IN UNDERSTANDING BIOLOGICAL, ECOLOGICAL, AND NATURAL SYSTEMS

Introduction

Layout of the Book

FUNDAMENTALS OF NEURAL NETWORKS AND MODELS FOR LINEAR DATA ANALYSIS

Introduction and Overview

Neural Networks and Their Capabilities

Inspirations from Biology

Modeling Information Processing in Neurons

Neuron Models and Learning Strategies

Models for Prediction and Classification

Practical Examples of Linear Neuron Models on Real Data

Comparison with Linear Statistical Methods

Summary

Problems

NEURAL NETWORKS FOR NONLINEAR PATTERN RECOGNITION

Overview and Introduction

Nonlinear Neurons

Practical Example of Modeling with Nonlinear Neurons

Comparison with Nonlinear Regression

One-Input Multilayer Nonlinear Networks

Two-Input Multilayer Perceptron Network

Case Studies on Nonlinear Classification and Prediction with Nonlinear Networks

Multidimensional Data Modeling with Nonlinear Multilayer Perceptron Networks

Summary

Problems

LEARNING OF NONLINEAR PATTERNS BY NEURAL NETWORKS

Introduction and Overview

Supervised Training of Networks for Nonlinear Pattern Recognition

Gradient Descent and Error Minimization

Backpropagation Learning and Illustration with an Example and Case Study

Delta-Bar-Delta Learning and Illustration with an Example and Case Study

Steepest Descent Method Presented with an Example

Comparison of First Order Learning Methods

Second-Order Methods of Error Minimization and Weight Optimization

Comparison of First Order and Second Order Learning Methods Illustrated through an Example

Summary

Problems

IMPLEMENTATION OF NEURAL NETWORK MODELS FOR EXTRACTING RELIABLE PATTERNS FROM DATA

Introduction and Overview

Bias-Variance Tradeoff

Illustration of Early Stopping and Regularization

Improving Generalization of Neural Networks

Network structure Optimization and Illustration with Examples

Reducing Structural Complexity of Networks by Pruning

Demonstration of Pruning with Examples

Robustness of a Network to Perturbation of Weights Illustrated Using an Example

Summary

Problems

DATA EXPLORATION, DIMENSIONALITY REDUCTION, AND FEATURE EXTRACTION

Introduction and Overview

Data Visualization Presented on Example Data

Correlation and Covariance between Variables

Normalization of Data

Example Illustrating Correlation, Covariance and Normalization

Selecting Relevant Inputs

Dimensionality Reduction and Feature Extraction

Example Illustrating Input Selection and Feature Extraction

Outlier Detection

Noise

Case Study: Illustrating Input Selection and Dimensionality Reduction for a

Practical Problem

Summary

Problems

ASSESSMENT OF UNCERTAINTY OF NEURAL NETWORK MODELS USING BAYESIAN STATISTICS

Introduction and Overview

Estimating Weight Uncertainty Using Bayesian Statistics

Case study Illustrating Weight Probability Distribution

Assessing Uncertainty of Neural Network Outputs Using Bayesian Statistics

Case Study Illustrating Uncertainty Assessment of Output Errors

Assessing the Sensitivity of Network Outputs to Inputs

Case Study Illustrating Uncertainty Assessment of Network Sensitivity to Inputs

Summary

Problems

DISCOVERING UNKNOWN CLUSTERS IN DATA WITH SELF-ORGANIZING MAPS

Introduction and Overview

Structure of Unsupervised Networks for Clustering Multidimensional Data

Learning in Unsupervised Networks

Implementation of Competitive Learning Illustrated Through Examples

Self-Organizing Feature Maps

Examples and Case Studies Using Self-Organizing Maps on Multi-Dimensional Data

Map Quality and Features Presented through Examples

Illustration of Forming Clusters on the Map and Cluster Characteristics

Map Validation and an Example

Evolving Self-Organizing Maps

Examples Illustrating Various Evolving Self Organizing Maps

Summary

Problems

NEURAL NETWORKS FOR TIME-SERIES FORECASTING

Introduction and Overview

Linear Forecasting of Time-Series with Statistical and Neural Network Models

Example Case Study

Neural Networks for Nonlinear Time-Series Forecasting

Example Case Study

Hybrid Linear (ARIMA) and Nonlinear Neural Network Models

Example Case Study

Automatic Generation of Network Structure Using Simplest Structure Concept-Illustrated Through Practical Application Case Study

Generalized Neuron Network and Illustration Through Practical Application Case

Study

Dynamically Driven Recurrent Networks

Practical Application Case Studies

Bias and Variance in Time-Series Forecasting Illustrated Through an Example

Long-Term Forecasting and a Case study

Input Selection for Time-Series Forecasting

Case study for Input Selection

Summary

Problems

Subject Categories

BISAC Subject Codes/Headings:
COM000000
COMPUTERS / General
COM051240
COMPUTERS / Software Development & Engineering / Systems Analysis & Design
SCI008000
SCIENCE / Life Sciences / Biology / General