Product 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.
An Instructor Support FTP site is available from the Wiley editorial department: ftp://ftp.ieee.org/uploads/press/hudson
From the Back Cover
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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