Applied Artificial Higher Order Neural Networks for Control and Recognition

✍ Scribed by Ming Zhang (editor)

• Publisher: IGI Global
• Year: 2016
• Tongue: English
• Leaves: 537
• Series: Advances in Computational Intelligence and Robotics
• Edition: Illustrated
• Category: Library

✦ Synopsis

In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification, and predition of control and recognition scenarios. Due to the fact that HONNs have been proven to be faster, more accurate, and easier to explain than traditional neural networks, their applications are limitless.

Applied Artificial Higher Order Neural Networks for Control and Recognition explores the ways in which higher order neural networks are being integrated specifically for intelligent technology applications. Emphasizing emerging research, practice, and real-world implementation, this timely reference publication is an essential reference source for researchers, IT professionals, and graduate-level computer science and engineering students.

✦ Table of Contents

Title Page
Copyright Page
Book Series
Dedication
Editorial Advisory Board
Table of Contents
Detailed Table of Contents
Preface
Acknowledgment
Section 1: Artificial Higher Order Neural Networks for Control
Chapter 1: Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator
Chapter 2: HONU and Supervised Learning Algorithms in Adaptive Feedback Control
Chapter 3: Novelty Detection in System Monitoring and Control with HONU
Section 2: Artificial Higher Order Neural Networks for Recognition
Chapter 4: Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition
Chapter 5: Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification
Chapter 6: Integration of Higher-Order Time-Frequency Statistics and Neural Networks
Section 3: Artificial Higher Order Neural Networks for Simulation and Predication
Chapter 7: Adaptive Hybrid Higher Order Neural Networks for Prediction of Stock Market Behavior
Chapter 8: Theoretical Analyses of the Universal Approximation Capability of a class of Higher Order Neural Networks based on Approximate Identity
Chapter 9: Artificial Sine and Cosine Trigonometric Higher Order Neural Networks for Financial Data Prediction
Chapter 10: Cosine and Sigmoid Higher Order Neural Networks for Data Simulations
Chapter 11: Improving Performance of Higher Order Neural Network using Artificial Chemical Reaction Optimization
Section 4: Artificial Higher Order Neural Network Models and Applications
Chapter 12: Artificial Higher Order Neural Network Models
Chapter 13: A Theoretical Framework for Parallel Implementation of Deep Higher Order Neural Networks
Chapter 14: Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights
Chapter 15: Utilizing Feature Selection on Higher Order Neural Networks
Chapter 16: Some Properties on the Capability of Associative Memory for Higher Order Neural Networks
Chapter 17: Discrete-Time Decentralized Inverse Optimal Higher Order Neural Network Control for a Multi-Agent Omnidirectional Mobile Robot
Chapter 18: Higher Order Neural Network for Financial Modeling and Simulation
Compilation of References
About the Contributors
Index