Recent Advances in Artificial Neural Networks

Series:
International Series on Computational Intelligence
Published:
March 09, 2000 by CRC Press - 376 Pages
Editor(s):
Lakhmi C. Jain, University of South Australia, Adelaide; A.M. Fanelli, Universita' di Bari, Bari, Italy
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ISBN 9780849322686
Cat# 2268
 

Features

  • Provides up-to-date coverage of neural network paradigms and applications
  • Presents the work of internationally renowned experts
  • Addresses many real-world applications such as pattern recognition, cluster detection and labeling, and process supervision
  • Offers information valuable to anyone interested in applying neural networks to real-world problems

    • Summary

    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.

    Table of Contents

    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.


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