Recent Advances in Artificial Neural Networks - Rilegato

Rilegato

ISBN 10: 0849322685 ISBN 13: 9780849322686

Casa editrice: CRC Press, 2000

Riassunto

Neural networks represent a new generation of information processing paradigms designed to mimic-in a very limited sense-the human brain. They can learn, recall, and generalize from training data, and with their potential applications limited only by the imaginations of scientists and engineers, they are commanding tremendous popularity and research interest. Over the last four decades, researchers have reported a number of neural network paradigms, however, the newest of these have not appeared in book form-until now. Recent Advances in Artificial Neural Networks collects the latest neural network paradigms and reports on their promising new applications. World-renowned experts discuss the use of neural networks in pattern recognition, color induction, classification, cluster detection, and more. Application engineers, scientists, and research students from all disciplines with an interest in considering neural networks for solving real-world problems will find this collection useful.

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Contenuti

A NEURO-SYMBOLIC HYBRID INTELLIGENT ARCHITECTURE WITH APPLICATIONS, J. Ghosh and I. Taha
Knowledge Based Module for Representation of Initial Domain Knowledge
Extraction of Supplementary Rules via the Statistical Analysis Module
The Mapping Module
The Discretization Module
Refining Input Characterization
Rule Extraction
Rule Evaluation and Ordering Procedure for the Refined Expert System
The Integrated Decision Maker
Application: Controlling Water Reservoirs
Application of the Statistical Approach
Discussion

NEW RADICAL BASIS NEURAL NETWORKS AND THEIR APPLICATION IN A LARGE-SCALE HANDWRITTEN DIGIT RECOGNITION PROBLEM, N.B. Karayiannis and S. Behnke
Function Approximation Models and RBF Neural Networks
Reformulating Radial Basis Neural Networks
Admissible Generator Functions
Selecting Generator Functions
Learning Algorithms Based on Gradient Descent
Generator Functions and Gradient Descent Learning
Handwritten Digit Recognition
Conclusions

EFFICIENT NEURAL NETWORK-BASED METHODOLOGY FOR THE DESIGN OF MULTIPLE CLASSIFIERS, N. Vassilas
Proposed Methodology
Modifications of Supervised Algorithms
Multimodular Classification
Land-Cover Classification
Summary

LEARNING FINE MOTION IN ROBOTICS: DESIGN AND EXPERIMENTS, C. Versino and L.M. Gambardella
How to Find the Path?
The Model-Based System
The Sensor-Based System
Perception Clustering
Action Triggering
All Together
Why Use a SOM-Like Network?
Planner vs. HEKM
Conclusions

A NEW NEURAL NETWORK FOR ADAPTIVE PATTERN RECOGNITION OF MULTICHANNEL INPUT SIGNALS, M. Fernández-Delgado, J. Presedo, M. Lama, and S. Barro
Architecture and Functionality of MART
Learning in MART
Analysis of the Behavior of Certain Adaptive Parameters
A Real Application Example
Discussion

LATERAL PRIMING ADAPTIVE RESONANCE THEORY (LAPART)-2: INNOVATION IN ART, T.P. Caudell and M.J. Healy
ART-1, Stacknet, and LAPART-1
The LAPART-2 Algorithm
The Learning Theorems
Numerical Experiments
Discussion
Conclusion

NEURAL NETWORK LEARNING IN A TRAVEL RESERVATION DOMAIN, H.A. Aboulenien and P. De Wilde
Agents
Neural Network Role
Agent Architecture
Operation
Summary

RECENT ADVANCES IN NEURAL NETWORK APPLICATIONS IN PROCESS CONTROL, U. Halici, K. Leblebicioglu, C. Özgen, and S. Tuncay
Process Control
Use of Neural Networks in Control
Case Study I: pH Control in Neutralization System
Case Study II: Adaptive Nonlinear-Model Predictive Control Using Neural Networks for Control of High Purity Industrial Distillation Column
Case Study III: PI Controller for a Batch Distillation Column with Neural Network Coefficient Estimator
Case Study IV: A Rule-Based Neuro-Optimal Controller for Steam-Jacketed Kettle
Remarks and Future Studies

MONITORING INTERNAL COMBUSTION ENGINES BY NEURAL NETWORK BASED VIRTUAL SENSING, R.J. Howlett, M.M. de Zoysa, and S.D. Walters
The Engine Management system
Virtual Sensor Systems
Air-Fuel Ratio
Combustion Monitoring Using the Spark Plug
The Ignition System of a Spark-Ignition Engine
Neural-Networks for Use in Virtual Sensors
AFR Estimation Using Neural Network Spark Voltage Characterization
Conclusions

NEURAL ARCHITECTURES OF FUZZY PETRI NETS, W. Pedrycz
Introduction
The Generalization of the Petri Net and its Underlying Architecture
The Architecture of the Fuzzy Petri Net
The Learning Procedure
Interfacing Fuzzy Petri Nets with Granular Information
Experiments
Conclusions

INDEX

Each chapter includes an introduction and references.

Product Description

Book by None

Dettagli dell?edizione

• Editore: CRC Press
• Data di pubblicazione: 2000
• Lingua: Inglese
• ISBN 10: 0849322685
• ISBN 13: 9780849322686
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 376
• Redattore: Jain Lakhmi C., Fanelli A.M.
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile