On the interpretation of fuzzy if then rules

Yager, Ronald R.

doi:10.1007/bf00117814

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…In the example of "if X value is A, then let Y value be B", A and B are verbal words and they indicate to which status X and Y values pertain to in fuzzy sets X and Y. As rules are processed in order, result found is processed to exits indicated by following equations and rules for new type fuzzy logic approach within the rules related with entry values made fuzzy themselves (Yager, 1996).…”

Section: Proposed Fuzzy Logic Methods and Application Proposed Method:mentioning

confidence: 99%

Comparison with Sugeno Model and Measurement Of Cancer Risk Analysis By New Fuzzy Logic Approach

Atnccedil¹,

Ylmaz²,

Kuuml³

et al. 2012

Afr. J. Biotechnol.

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Every year thousands of human mortality from cancer is due to limitation of medical sources and unable to use the existing sources effectively. Patient losses can be reduced by using the numerical (quantitative) techniques in the system of medical and health. Cancer is the leading life-threatening disease for people in today's world. Although cancer formation is different for each type of cancer, it is determined in studies and research conducted that stress also triggers cancer types. Early precaution is very important for the people who have not been sick yet that have high mortality rate and expensive treatment such as cancer. With this type of study, the possibility of getting disease may decrease and people can take measures for the disease. In this study, for the three cancer types selected as pilot by introducing a new type of fuzzy logic model, the opportunity of revealing of risks for catching these cancer types of people and the opportunity of providing preliminary diagnosis to the person to remove this risk are presented. After the calculation of risk outcome, the effect of stress on cancer is discussed and calculated. Due to this type of study, people will have the chance to take measures against catching cancer and the rate of catching cancer can be decreased. Due to this study, the presentation of strong software is aimed, so that related techniques are used in the health field and sample studies are conducted. Furthermore, the performance status of the new technique was revealed by calculating performance measurements of the outcomes of the models developed by the new type of fuzzy logic technique for three cancer types selected as pilot within the study and TakagiSugeno type of fuzzy logic model.

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Section: Proposed Fuzzy Logic Methods and Application Proposed Method:mentioning

confidence: 99%

Comparison with Sugeno Model and Measurement Of Cancer Risk Analysis By New Fuzzy Logic Approach

Atnccedil¹,

Ylmaz²,

Kuuml³

et al. 2012

Afr. J. Biotechnol.

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“…Hence, the assumptions bring a filtering effect, which may loose the vital imprecise parts of the basic information. Therefore, the fuzzy logic approach has become convenient for modelling such situations (Dubois & Prade, 1991, 1996Mamdani, 1977;Russo & Jain, 2001;Wang & Mendel, 1992;Yager, 1996;Zadeh, 1968). …”

Section: Fuzzy Systems and Rulesmentioning

confidence: 98%

Fuzzy system modelling of drinking water consumption prediction

Şen

Altunkaynak

2009

Expert Systems with Applications

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“…If the harmony variable is obtained from HM, then fine-tune the harmony variable. Specifically, a random number r2 is generated between [0,1]…”

Section: A Basic Harmony Search (Hs) Algorithmmentioning

confidence: 99%

“…Production Rule with 'IF-THEN' formalism is one of the most common representation forms of knowledge in the field of artificial intelligence, which not only has the advantages to understand and to add easily but also to delete or to update related information [1]. To improve the representation ability of traditional production rule, a weighted fuzzy production rule (WFPR) was produced to express vague knowledge [2].…”

Section: Introductionmentioning

confidence: 99%

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Zhou

et al. 2020

IEEE Access

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Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.

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On the interpretation of fuzzy if then rules

Cited by 15 publications

References 17 publications

Comparison with Sugeno Model and Measurement Of Cancer Risk Analysis By New Fuzzy Logic Approach

Comparison with Sugeno Model and Measurement Of Cancer Risk Analysis By New Fuzzy Logic Approach

Fuzzy system modelling of drinking water consumption prediction

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

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