Neural Networks and Artificial Intelligence for Biomedical Engineering / Edition 1

Neural Networks and Artificial Intelligence for Biomedical Engineering / Edition 1

by Donna L. Hudson, Maurice E. Cohen
     
 

Biomedical/Electrical Engineering Neural Networks and Artificial Intelligence for Biomedical Engineering Using examples drawn from biomedicine and biomedical engineering, this reference text provides comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for

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Overview

Biomedical/Electrical Engineering Neural Networks and Artificial Intelligence for Biomedical Engineering Using examples drawn from biomedicine and biomedical engineering, this reference text provides comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision-making aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques currently used with a wide range of biomedical applications. Highlighted topics include:

  • Types of neural networks and neural network algorithms
  • Knowledge-based representation and acquisition
  • Reasoning methodologies and searching strategies
  • Chaotic analysis of biomedical time series
  • Genetic algorithms
  • Probability-based systems and fuzzy systems
  • Case study and MATLAB® exercises
  • Evaluation and validation of decision support aids

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Product Details

ISBN-13:
9780780334045
Publisher:
Wiley
Publication date:
10/28/1999
Series:
IEEE Press Series on Biomedical Engineering Series, #3
Edition description:
New Edition
Pages:
340
Product dimensions:
7.30(w) x 10.20(h) x 0.92(d)

Table of Contents

Preface.

Acknowledgments.

Overview.

NEURAL NETWORKS.

Foundations of Neural Networks.

Classes of Neural Networks.

Classification Networks and Learning.

Supervised Learning.

Unsupervised Learning.

Design Issues.

Comparative Analysis.

Validation and Evaluation.

ARTIFICIAL INTELLIGENCE.

Foundation of Computer-Assisted Decision Making.

Knowledge Representation.

Knowledge Acquisition.

Reasoning Methodologies.

Validation and Evaluation.

ALTERNATIVE APPROACHES.

Genetic Algorithms.

Probabilistic Systems.

Fuzzy Systems.

Hybrid Systems.

HyperMerge, a Hybird Expert System.

Future Perspectives.

Index.

About the Authors.

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