A H y b r i d F u z z y - دانلود رایگان
دانلود رایگان تعداد صفحه : 28A H y b r i d F u z z y - N e u r a l E x p e r t S y s t e m f o r D i a
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| A H y b r i d F u z z yA H y b r i d F u z z y - N e u r a l E x p e r t S y s t e m f o r D i a g n o s i s Christoph S. Herrmann * Intellektik, Informatik, TH Darmstadt Alexanderstrafie 10, D-64283 Darmstadt, Germany [email protected] Abstract Fuzzy Logic, a neural network and an expert system are combined to build a hybrid diagnosis system. With this system we introduce a new approach to the acquisition of knowledge bases. Our system consists of a fuzzy expert system with a dual source knowledge base. Two sets of rules are acquired, inductively from given examples and deductively formulated by a physician. A fuzzy neural network serves to learn from sample data and allows to extract fuzzy rules for the knowledge base. The diagnosis of electroencephalograms by interpretation of graphoelements serves as visualization for our approach. Preliminary results demonstrate the promising possibilities offered by our method. 1 Introduction Repetitively applied cognitive tasks of recognizing and evaluating certain phenomena, called diagnostic tasks, are among the main applications for Artificial Intelligence (AI). As there exists a vast variety of such diagnostic tasks in medicine, it has always belonged to the spectrum of potential users of Artificial Intelligence. Most popular among AI methods in medicine are knowledge based systems [Buchanan and Shortliffe, 1985], modeling the diagnostic behaviour of experts. A variety of such expert systems is being used in everyday practice of physicians since Shortliffe introduced MYCIN Shortliffe, 1976], an expert system designed to diagnose infections of the human blood. One of the greatest difficulties in designing a convenient expert system is acquiring the knowledge base. We introduce a new approach where a dual source knowledge base is generated by deductive دریافت فایل جهت کپی مطلب از ctrl+A استفاده نمایید نماید |