Get Neural Networks and Artificial Intelligence for Biomedical PDF
By Donna L. Hudson, Maurice E. Cohen
Utilizing examples drawn from biomedicine and biomedical engineering, this crucial reference publication brings you complete assurance of the entire significant concepts at present on hand to construct computer-assisted choice help platforms. you will discover functional suggestions for biomedicine in line with present thought and functions of neural networks, synthetic intelligence, and different equipment for the advance of selection aids, together with hybrid platforms.
Neural Networks and synthetic Intelligence for Biomedical Engineering deals scholars and scientists of biomedical engineering, biomedical informatics, and clinical man made intelligence a deeper figuring out of the robust innovations now in use with quite a lot of biomedical functions.
Highlighted issues contain:
- Types of neural networks and neural community algorithms
- Knowledge illustration, wisdom acquisition, and reasoning methodologies
- Chaotic research of biomedical time sequence
- Genetic algorithms
- Probability-based platforms and fuzzy structures
- Evaluation and validation of determination help aids.
An teacher aid FTP web site is accessible from the Wiley editorial division: ftp://ftp.ieee.org/uploads/press/hudsonContent:
Chapter 1 Foundations of Neural Networks (pages 11–28):
Chapter 2 sessions of Neural Networks (pages 29–44):
Chapter three type Networks and studying (pages 45–57):
Chapter four Supervised studying (pages 59–77):
Chapter five Unsupervised studying (pages 79–93):
Chapter 6 layout matters (pages 95–107):
Chapter 7 Comparative research (pages 109–119):
Chapter eight Validation and assessment (pages 121–127):
Chapter nine Foundations of Computer?Assisted selection Making (pages 129–149):
Chapter 10 wisdom illustration (pages 151–172):
Chapter eleven wisdom Acquisition (pages 173–184):
Chapter 12 Reasoning Methodologies (pages 185–204):
Chapter thirteen Validation and review (pages 205–213):
Chapter 14 Genetic Algorithms (pages 215–224):
Chapter 15 Probabilistic structures (pages 225–242):
Chapter sixteen Fuzzy structures (pages 243–260):
Chapter 17 Hybrid structures (pages 261–271):
Chapter 18 HyperMerge, a Hybrid specialist method (pages 273–290):
Chapter 19 destiny views (pages 291–295):
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Additional resources for Neural Networks and Artificial Intelligence for Biomedical Engineering
This is not the only possible structure. As we will see in subsequent chapters, it is possible to have multiple output nodes and even output nodes that feed into other types of decision-making strategies, such as symbolic reasoning. 7 SUMMARY In this chapter we have reviewed some of the components of biological nervous systems that are important contributors to the foundations of artificial neural networks. In addition to these biological precursors, the most important technical precursor to References 27 neural networks, pattern classification, which was used successfully for many years in design of medical decision-making aids, was summarized.
1 Feed-Forward Networks. 4 apply to feed-forward networks. These networks compute weights that are used to determine output from a node that is subsequently fed to the next layer. 8 Three-Layer Neural Network Structure. given earlier, the weights determined the impact that the input nodes have on the output, but no information is fed back to the input nodes. 2 Feed-Backward Networks. The revival of neural networks began in the early 1980s with the work of Hopfield (1982). The Hopfield model was completely different from earlier approaches in that the neurons, or nodes, had two-way connections.
These models may use supervised learning in which the cate- Chapter 2 ■ Classes of Neural Networks 32 gories are known or unsupervised learning in which the categories may not be known. Association Models: The two types of association models are auto-association, which focuses on the retrieval of an object based on part of the object itself; and hetero-association, which focuses on the retrieval of an object in one set using an object in a different set. Optimization: The objective of these systems is to find the best solution by minimizing a cost function or other measure.