Neural Networks and Artificial Intelligence for Biomedical Engineering

✍ Scribed by Donna L. Hudson, Maurice E. Cohen(auth.)

• Publisher: Wiley-IEEE Press
• Year: 1999
• Tongue: English
• Leaves: 314
• Category: Library

✦ Synopsis

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.

An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/hudsonContent:
Chapter 1 Foundations of Neural Networks (pages 11–28):
Chapter 2 Classes of Neural Networks (pages 29–44):
Chapter 3 Classification Networks and Learning (pages 45–57):
Chapter 4 Supervised Learning (pages 59–77):
Chapter 5 Unsupervised Learning (pages 79–93):
Chapter 6 Design Issues (pages 95–107):
Chapter 7 Comparative Analysis (pages 109–119):
Chapter 8 Validation and Evaluation (pages 121–127):
Chapter 9 Foundations of Computer?Assisted Decision Making (pages 129–149):
Chapter 10 Knowledge Representation (pages 151–172):
Chapter 11 Knowledge Acquisition (pages 173–184):
Chapter 12 Reasoning Methodologies (pages 185–204):
Chapter 13 Validation and Evaluation (pages 205–213):
Chapter 14 Genetic Algorithms (pages 215–224):
Chapter 15 Probabilistic Systems (pages 225–242):
Chapter 16 Fuzzy Systems (pages 243–260):
Chapter 17 Hybrid Systems (pages 261–271):
Chapter 18 HyperMerge, a Hybrid Expert System (pages 273–290):
Chapter 19 Future Perspectives (pages 291–295):