Neural networks and artificial intelligence for biomedical engineering
Author(s)
Bibliographic Information
Neural networks and artificial intelligence for biomedical engineering
(IEEE Press series in biomedical engineering)
IEEE Press, c2000
Available at 13 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
Using examples drawn from biomedicine and biomedical engineering, this essential reference book brings you 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 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 now in use with a wide range of biomedical applications.
Highlighted topics include:
Types of neural networks and neural network algorithms
Knowledge representation, knowledge acquisition, and reasoning methodologies
Chaotic analysis of biomedical time series
Genetic algorithms
Probability-based systems and fuzzy systems
Evaluation and validation of decision support aids
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.
by "Nielsen BookData"