دانلود رایگان مقاله انگلیسی تشخیص بیماری های قلبی بر اساس منطق فازی چند رسانه ای - الزویر 2018

عنوان فارسی
تشخیص بیماری های قلبی بر اساس منطق فازی چند رسانه ای
عنوان انگلیسی
Heart disease diagnosis based on mediative fuzzy logic
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
10
سال انتشار
2018
نشریه
الزویر - Elsevier
فرمت مقاله انگلیسی
PDF
کد محصول
E8763
رشته های مرتبط با این مقاله
پزشکی
گرایش های مرتبط با این مقاله
انفورماتیک پزشکی، قلب و عروق
مجله
هوش مصنوعی در پزشکی - Artificial Intelligence In Medicine
دانشگاه
University of Craiova - Department of Computer Science - A. I. Cuza Street - Romania
کلمات کلیدی
مجموعه فازی، مجموعه فازی شهودی، منطق فازی رسانه ای، کنترل منطقی فازی
۰.۰ (بدون امتیاز)
امتیاز دهید
چکیده

ABSTRACT


Mediative fuzzy logic is an approach able to deal with inconsistent information providing a solution when contradiction exists. The aim of this paper is to design an expert system based on this type of fuzzy logic in order to diagnose a possible heart disease for a patient. Our proposed system is an extension of the standard Mamdani fuzzy logic controller and contains 44 rules of the type single input–single output. The system works with 11 variables as inputs and one variable as output.

نتیجه گیری

6. Conclusion


Uncertainty appears in different forms and. affects decision making. Nowadays, there are mathematical models to handle the uncertainty. But if we work with a knowledge base that changes with time, and with non-contradictory information that becomes doubtful or contradictory, or with any combination of these three situations then we need to use mediative fuzzy logic which is able to process inconsistent information.


So is mentioned in first chapter, applications based on mediative fuzzy logic (see papers [17–22]) have shown its superiority to other fuzzy logics (traditional or intuitive). For this reason, in this paper, we extends and improves the system from [30] based on fuzzy logic by working with intuitionistic fuzzy sets to represent the input and output variables and with mediative fuzzy logic for reasoning. Superiority of our system is given by the possibility to handle contradictory and doubtful information. As is mentioned in [22] the fact of having the possibility of complementing the knowledge with non-agreement functions give us the possibility of implementing a more realistic fuzzy inference system.


In future papers we intend to improve this system by


• tuning the membership functions and rules used in inference system; for instance, if-then rules can be obtained from training patterns


• using a procedure to generate inference rules so that each has a degree of certainty


• improving the reasoning system by using the degree of certainty for determining the inferred conclusion


• test with other equations to compute the mediate output; for instance (8) and (9).


بدون دیدگاه