Description
Book SynopsisUsing 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
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.
