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New Hybrid Intelligent Systems For Diagnosis And Risk Evaluation Of Arterial Hypertension Melin

  • SKU: BELL-6752654
New Hybrid Intelligent Systems For Diagnosis And Risk Evaluation Of Arterial Hypertension Melin
$ 31.00 $ 45.00 (-31%)

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New Hybrid Intelligent Systems For Diagnosis And Risk Evaluation Of Arterial Hypertension Melin instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 3.7 MB
Pages: 92
Author: Melin, Patricia; Prado-Arechiga, German
ISBN: 9783319611488, 9783319611495, 3319611488, 3319611496
Language: English
Year: 2018

Product desciption

New Hybrid Intelligent Systems For Diagnosis And Risk Evaluation Of Arterial Hypertension Melin by Melin, Patricia; Prado-arechiga, German 9783319611488, 9783319611495, 3319611488, 3319611496 instant download after payment.

In this book, a new approach for diagnosis and risk evaluation of ar-terial hypertension is introduced. The new approach was implement-ed as a hybrid intelligent system combining modular neural net-works and fuzzy systems. The different responses of the hybrid system are combined using fuzzy logic. Finally, two genetic algo-rithms are used to perform the optimization of the modular neural networks parameters and fuzzy inference system parameters. The experimental results obtained using the proposed method on real pa-tient data show that when the optimization is used, the results can be better than without optimization. This book is intended to be a refer-ence for scientists and physicians interested in applying soft compu-ting techniques, such as neural networks, fuzzy logic and genetic algorithms, in medical diagnosis, but also in general to classification and pattern recognition and similar problems.
Abstract: In this book, a new approach for diagnosis and risk evaluation of ar-terial hypertension is introduced. The new approach was implement-ed as a hybrid intelligent system combining modular neural net-works and fuzzy systems. The different responses of the hybrid system are combined using fuzzy logic. Finally, two genetic algo-rithms are used to perform the optimization of the modular neural networks parameters and fuzzy inference system parameters. The experimental results obtained using the proposed method on real pa-tient data show that when the optimization is used, the results can be better than without optimization. This book is intended to be a refer-ence for scientists and physicians interested in applying soft compu-ting techniques, such as neural networks, fuzzy logic and genetic algorithms, in medical diagnosis, but also in general to classification and pattern recognition and similar problems

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