New hybrid intelligent systems for diagnosis and risk evaluation of arterial hypertension
- 作者： Melin, Patricia, author.
- 其他作者：
- 其他題名：
- SpringerBriefs in applied sciences and technology.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： SpringerBriefs in applied sciences and technology,
- 主題： Hypertension--Diagnosis. , Hypertension--Risk assessment. , Neural networks (Computer science) , Soft computing. , Fuzzy logic. , Engineering. , Computational Intelligence. , Biomedical Engineering. , Health Informatics.
- ISBN： 9783319611495 (electronic bk.) 、 9783319611488 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: From the Content: Introduction -- Fuzzy Logic for Arterial Hypertension Classification -- Design of a Neuro Design of a Neuro Design of Arterial Hypertension.
- 摘要註: 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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讀者標籤：
- 系統號： 005419149 | 機讀編目格式
館藏資訊
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.