Neural Networks for Applied Sciences and Engineering From Fundamentals to Complex Pattern Recognition

ISBN-10: 084933375X

ISBN-13: 9780849333750

Edition: 2007

Authors: Sandhya Samarasinghe

List price: $139.95
eBook available
30 day, 100% satisfaction guarantee

If an item you ordered from TextbookRush does not meet your expectations due to an error on our part, simply fill out a return request and then return it by mail within 30 days of ordering it for a full refund of item cost.

Learn more about our returns policy


In response to an increasing demand for novel computing methods, Neural Networks for Applied Sciences and Engineering provides a simple but systematic introduction to neural networks applications. This book features case studies that use real data to demonstrate practical applications. It contains in-depth discussions of data and model validation issues along with uncertainty and sensitivity assessment of models as well as data dimensionality and methods to reduce dimensionality. It provides detailed coverage of neural network types for extracting nonlinear patterns in multi-dimensional scientific data in prediction, classification, clustering and forecasting with an extensive coverage on linear networks, multi-layer perceptron, self organization maps, and recurrent networks.
eBooks Starting from $55.98
Rent eBooks
Buy eBooks
what's this?
Rush Rewards U
Members Receive:
You have reached 400 XP and carrot coins. That is the daily max!
Study Briefs

Limited time offer: Get the first one free! (?)

All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.

Add to cart
Study Briefs
Periodic Table Online content $4.95 $1.99
Add to cart
Study Briefs
SQL Online content $4.95 $1.99
Add to cart
Study Briefs
MS Excel® 2010 Online content $4.95 $1.99
Add to cart
Study Briefs
MS Word® 2010 Online content $4.95 $1.99
Customers also bought

Book details

List price: $139.95
Copyright year: 2007
Publisher: Auerbach Publishers, Incorporated
Publication date: 9/12/2006
Binding: Hardcover
Pages: 570
Size: 6.25" wide x 9.25" long x 1.50" tall
Weight: 2.134
Language: English

About the Author
From Data to Models: Complexity and Challenges in Understanding Biological, Ecological, and Natural Systems
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
Threshold Neuron as a Simple Classifier
Learning Models for Neurons and Neural Assemblies
Hebbian Learning
Unsupervised or Competitive Learning
Supervised Learning
Perceptron with Supervised Learning as a Classifier
Perceptron Learning Algorithm
A Practical Example of Perceptron on a Larger Realistic Data Set: Identifying the Origin of Fish from the Growth-Ring Diameter of Scales
Comparison of Perceptron with Linear Discriminant Function Analysis in Statistics
Multi-Output Perceptron for Multicategory Classification
Higher-Dimensional Classification Using Perceptron
Perceptron Summary
Linear Neuron for Linear Classification and Prediction
Learning with the Delta Rule
Linear Neuron as a Classifier
Classification Properties of a Linear Neuron as a Subset of Predictive Capabilities
Example: Linear Neuron as a Predictor
A Practical Example of Linear Prediction: Predicting the Heat Influx in a Home
Comparison of Linear Neuron Model with Linear Regression
Example: Multiple Input Linear Neuron Model-Improving the Prediction Accuracy of Heat Influx in a Home
Comparison of a Multiple-Input Linear Neuron with Multiple Linear Regression
Multiple Linear Neuron Models
Comparison of a Multiple Linear Neuron Network with Canonical Correlation Analysis
Linear Neuron and Linear Network Summary
Neural Networks for Nonlinear Pattern Recognition
Overview and Introduction
Multilayer Perceptron
Nonlinear Neurons
Neuron Activation Functions
Sigmoid Functions
Gaussian Functions
Example: Population Growth Modeling Using a Nonlinear Neuron
Comparison of Nonlinear Neuron with Nonlinear Regression Analysis
One-Input Multilayer Nonlinear Networks
Processing with a Single Nonlinear Hidden Neuron
Examples: Modeling Cyclical Phenomena with Multiple Nonlinear Neurons
Example 1: Approximating a Square Wave
Example 2: Modeling Seasonal Species Migration
Two-Input Multilayer Perceptron Network
Processing of Two-Dimensional Inputs by Nonlinear Neurons
Network Output
Examples: Two-Dimensional Prediction and Classification
Example 1: Two-Dimensional Nonlinear Function Approximation
Example 2: Two-Dimensional Nonlinear Classification Model
Multidimensional Data Modeling with Nonlinear Multilayer Perceptron Networks
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
Example: Backpropagation Training-A Hand Computation
Error Gradient with Respect to Output Neuron Weights
The Error Gradient with Respect to the Hidden-Neuron Weights
Application of Gradient Descent in Backpropagation Learning
Batch Learning
Learning Rate and Weight Update
Example-by-Example (Online) Learning
Example: Backpropagation Learning Computer Experiment
Single-Input Single-Output Network with Multiple Hidden Neurons
Multiple-Input, Multiple-Hidden Neuron, and Single-Output Network
Multiple-Input, Multiple-Hidden Neuron, Multiple-Output Network
Example: Backpropagation Learning Case Study-Solving a Complex Classification Problem
Delta-Bar-Delta Learning (Adaptive Learning Rate) Method
Example: Network Training with Delta-Bar-Delta-A Hand Computation
Example: Delta-Bar-Delta with Momentum-A Hand Computation
Network Training with Delta-Bar Delta-A Computer Experiment
Comparison of Delta-Bar-Delta Method with Backpropagation
Example: Network Training with Delta-Bar-Delta-A Case Study
Steepest Descent Method
Example: Network Training with Steepest Descent-Hand Computation
Example: Network Training with Steepest Descent-A Computer Experiment
Second-Order Methods of Error Minimization and Weight Optimization
Example: Network Training with QuickProp-A Hand Computation
Example: Network Training with QuickProp-A Computer Experiment
Comparison of QuickProp with Steepest Descent, Delta-Bar-Delta, and Backpropagation
General Concept of Second-Order Methods of Error Minimization
Gauss-Newton Method
Network Training with the Gauss-Newton Method-A Hand Computation
Example: Network Training with Gauss-Newton Method-A Computer Experiment
The Levenberg-Marquardt Method
Example: Network Training with LM Method-A Hand Computation
Network Training with the LM Method-A Computer Experiment
Comparison of the Efficiency of the First-Order and Second-Order Methods in Minimizing Error
Comparison of the Convergence Characteristics of First-Order and Second-Order Learning Methods
Steepest Descent Method
Gauss-Newton Method
Levenberg-Marquardt Method
Implementation of Neural Network Models for Extracting Reliable Patterns from Data
Introduction and Overview
Bias-Variance Tradeoff
Improving Generalization of Neural Networks
Illustration of Early Stopping
Effect of Initial Random Weights
Weight Structure of the Trained Networks
Effect of Random Sampling
Effect of Model Complexity: Number of Hidden Neurons
Summary on Early Stopping
Reducing Structural Complexity of Networks by Pruning
Optimal Brain Damage
Example of Network Pruning with Optimal Brain Damage
Network Pruning Based on Variance of Network Sensitivity
Illustration of Application of Variance Nullity in Pruning Weights
Pruning Hidden Neurons Based on Variance Nullity of Sensitivity
Robustness of a Network to Perturbation of Weights
Confidence Intervals for Weights
Data Exploration, Dimensionality Reduction, and Feature Extraction
Introduction and Overview
Example: Thermal Conductivity of Wood in Relation to Correlated Input Data
Data Visualization
Correlation Scatter Plots and Histograms
Parallel Visualization
Projecting Multidimensional Data onto Two-Dimensional Plane
Correlation and Covariance between Variables
Normalization of Data
Simple Range Scaling
Whitening-Normalization of Correlated Multivariate Data
Selecting Relevant Inputs
Statistical Tools for Variable Selection
Partial Correlation
Multiple Regression and Best-Subsets Regression
Dimensionality Reduction and Feature Extraction
Principal Component Analysis (PCA)
Partial Least-Squares Regression
Outlier Detection
Case Study: Illustrating Input Selection and Dimensionality Reduction for a Practical Problem
Data Preprocessing and Preliminary Modeling
PCA-Based Neural Network Modeling
Effect of Hidden Neurons for Non-PCA- and PCA-Based Approaches
Case Study Summary
Assessment of Uncertainty of Neural Network Models Using Bayesian Statistics
Introduction and Overview
Estimating Weight Uncertainty Using Bayesian Statistics
Quality Criterion
Incorporating Bayesian Statistics to Estimate Weight Uncertainty
Square Error
Intrinsic Uncertainty of Targets for Multivariate Output
Probability Density Function of Weights
Example Illustrating Generation of Probability Distribution of Weights
Estimation of Geophysical Parameters from Remote Sensing: A Case Study
Assessing Uncertainty of Neural Network Outputs Using Bayesian Statistics
Example Illustrating Uncertainty Assessment of Output Errors
Total Network Output Errors
Error Correlation and Covariance Matrices
Statistical Analysis of Error Covariance
Decomposition of Total Output Error into Model Error and Intrinsic Noise
Assessing the Sensitivity of Network Outputs to Inputs
Approaches to Determine the Influence of Inputs on Outputs in Feedforward Networks
Methods Based on Magnitude of Weights
Sensitivity Analysis
Example: Comparison of Methods to Assess the Influence of Inputs on Outputs
Uncertainty of Sensitivities
Example Illustrating Uncertainty Assessment of Network Sensitivity to Inputs
PCA Decomposition of Inputs and Outputs
PCA-Based Neural Network Regression
Neural Network Sensitivities
Uncertainty of Input Sensitivity
PCA-Regularized Jacobians
Case Study Summary
Discovering Unknown Clusters in Data with Self-Organizing Maps
Introduction and Overview
Structure of Unsupervised Networks
Learning in Unsupervised Networks
Implementation of Competitive Learning
Winner Selection Based on Neuron Activation
Winner Selection Based on Distance to Input Vector
Other Distance Measures
Competitive Learning Example
Recursive Versus Batch Learning
Illustration of the Calculations Involved in Winner Selection
Network Training
Self-Organizing Feature Maps
Learning in Self-Organizing Map Networks
Selection of Neighborhood Geometry
Training of Self-Organizing Maps
Neighbor Strength
Example: Training Self-Organizing Networks with a Neighbor Feature
Neighbor Matrix and Distance to Neighbors from the Winner
Shrinking Neighborhood Size with Iterations
Learning Rate Decay
Weight Update Incorporating Learning Rate and Neighborhood Decay
Recursive and Batch Training and Relation to K-Means Clustering
Two Phases of Self-Organizing Map Training
Example: Illustrating Self-Organizing Map Learning with a Hand Calculation
SOM Case Study: Determination of Mastitis Health Status of Dairy Herd from Combined Milk Traits
Example of Two-Dimensional Self-Organizing Maps: Clustering Canadian and Alaskan Salmon Based on the Diameter of Growth Rings of the Scales
Map Structure and Initialization
Map Training
Map Initialization
Example: Training Two-Dimensional Maps on Multidimensional Data
Data Visualization
Map Structure and Training
Point Estimates of Probability Density of Inputs Captured by the Map
Quantization Error
Accuracy of Retrieval of Input Data from the Map
Forming Clusters on the Map
Approaches to Clustering
Example Illustrating Clustering on a Trained Map
Finding Optimum Clusters on the Map with the Ward Method
Finding Optimum Clusters by K-Means Clustering
Validation of a Trained Map
n-Fold Cross Validation
Evolving Self-Organizing Maps
Growing Cell Structure of Map
Centroid Method for Mapping Input Data onto Positions between Neurons on the Map
Dynamic Self-Organizing Maps with Controlled Growth (GSOM)
Example: Application of Dynamic Self-Organizing Maps
Evolving Tree
Neural Networks for Time-Series Forecasting
Introduction and Overview
Linear Forecasting of Time-Series with Statistical and Neural Network Models
Example Case Study: Regulating Temperature of a Furnace
Multistep-Ahead Linear Forecasting
Neural Networks for Nonlinear Time-Series Forecasting
Focused Time-Lagged and Dynamically Driven Recurrent Networks
Focused Time-Lagged Feedforward Networks
Spatio-Temporal Time-Lagged Networks
Example: Spatio-Temporal Time-Lagged Network-Regulating Temperature in a Furnace
Single-Step Forecasting with Neural NARx Model
Multistep Forecasting with Neural NARx Model
Case Study: River Flow Forecasting
Linear Model for River Flow Forecasting
Nonlinear Neural (NARx) Model for River Flow Forecasting
Input Sensitivity
Hybrid Linear (ARIMA) and Nonlinear Neural Network Models
Case Study: Forecasting the Annual Number of Sunspots
Automatic Generation of Network Structure Using Simplest Structure Concept
Case Study: Forecasting Air Pollution with Automatic Neural Network Model Generation
Generalized Neuron Network
Case Study: Short-Term Load Forecasting with a Generalized Neuron Network
Dynamically Driven Recurrent Networks
Recurrent Networks with Hidden Neuron Feedback
Encapsulating Long-Term Memory
Structure and Operation of the Elman Network
Training Recurrent Networks
Network Training Example: Hand Calculation
Recurrent Learning Network Application Case Study: Rainfall Runoff Modeling
Two-Step-Ahead Forecasting with Recurrent Networks
Real-Time Recurrent Learning Case Study: Two-Step-Ahead Stream Flow Forecasting
Recurrent Networks with Output Feedback
Encapsulating Long-Term Memory in Recurrent Networks with Output Feedback
Application of a Recurrent Net with Output and Error Feedback and Exogenous Inputs: (NARIMAx) Case Study: Short-Term Temperature Forecasting
Training of Recurrent Nets with Output Feedback
Fully Recurrent Network
Fully Recurrent Network Practical Application Case Study: Short-Term Electricity Load Forecasting
Bias and Variance in Time-Series Forecasting
Decomposition of Total Error into Bias and Variance Components
Example Illustrating Bias-Variance Decomposition
Long-Term Forecasting
Case Study: Long-Term Forecasting with Multiple Neural Networks (MNNs)
Input Selection for Time-Series Forecasting
Input Selection from Nonlinearly Dependent Variables
Partial Mutual Information Method
Generalized Regression Neural Network
Self-Organizing Maps for Input Selection
Genetic Algorithms for Input Selection
Practical Application of Input Selection Methods for Time-Series Forecasting
Input Selection Case Study: Selecting Inputs for Forecasting River Salinity
Free shipping on orders over $35*

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

Learn more about the TextbookRush Marketplace.