Neural networks for applied sciences and engineering : from fundamentals to complex pattern recognition /: from fundamentals to complex pattern recognition. ([2016?])

• Record Type: Book
• Further Information: Note: Sandhya Samarasinghe.
• Authors: Samarasinghe, Sandhya
• 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
• Publisher Details: Boca Raton, FL : Auerbach Publications
• Publication Date: 2016
• Copyright Date: 2007
• Extent: 1 online resource, illustrations
• Subjects: 006.3/2; Neural networks (Computer science); Pattern recognition systems; Neural Networks (Computer); Pattern Recognition, Automated; COMPUTERS -- Neural Networks; Neural networks (Computer science); Pattern recognition systems; Fulltext; Internet Resource; Illustration; Electronic books; Electronic books
• Languages: English
• ISBNs: 9781420013061; 1420013068
• Notes: Note: Includes bibliographical references and index.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.