fuzzy logic has a high potential for managing the uncertainty sources associated with the medical expert systems. application of fuzzy inference model has been widely concentrated for managing uncertainties in computer based practices of medicine. this paper has proposed two fuzzy expert systems for prognosis of the heart disease based on: 1) mamdani inference model, and 2) sugeno inference mod...

Load sensor is developed using mamdani fuzzy inference system and sugeno fuzzy inference system. It is two input and one output sensor. Both mamdani-type fuzzy inference system and sugeno-type fuzzy inference system are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between these two fuzzy inference system and their simulated results are compared. Index Ter...

Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion on Simulation Mechanism Based (SMB) classification method and composite patterns, this paper presents the Mamdani model based Adaptive Neural Fuzzy Inference System (M-ANFIS) and weight updating formula in consideration with qualitative representation of inference consequent parts in fuzzy neura...

The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering...

In this paper, we propose a generalized fuzzy inference system (GFIS) in noise image processing. The GFIS is a multi-layer neuro-fuzzy structure which combines both Mamdani model and TS fuzzy model to form a hybrid fuzzy system. The GFIS can not only preserve the interpretability property of the Mamdani model but also keep the robust local stability criteria of the TS model. Simulation results ...

The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. Those rules are not explicit for the expert. This paper proposes a new method to generate Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps. The first step consists in using the subtractive clustering ...

A fuzzy control system which is a typical system utilizing fuzzy model is mainly using the Max-Min CRI (Compositional Rule of Inference) method by Zadeh and Mamdani for fuzzy inference. But the Max-Min CRI method suffers from drawbacks including: error-prone weighting strategy, inefficient compositional rule of inference, and subjective formulation of membership functions. Because of these prob...

This study presents a novel controller of magnetic levitation system by using new neuro-fuzzy structures which called flexible neuro-fuzzy systems. In this type of controller we use sliding mode control with neuro-fuzzy to eliminate the Jacobian of plant. At first, we control magnetic levitation system with Mamdanitype neuro-fuzzy systems and logical-type neuro-fuzzy systems separately and then...

The database of a rule-based systemmay contain imprecisionswhich appear in the description of the rules given by the expert. Because such an inference can not be made by the methods which use classical two valued logic or many valued logic, Zadeh in (Zadeh, 1975) and Mamdani in (Mamdani, 1977) suggested an inference rule called "compositional rule of inference". Using this inference rule, sever...

The paper presents the design of municipal creditworthiness parameters. Municipal creditworthiness modelling is realized by fuzzy logic based systems. Therefore, current designs of hierarchical structures of Mamdanitype fuzzy inference systems and their analysis are introduced. Key-Words: Municipal creditworthiness, Mamdani-type fuzzy inference systems, hierarchical structures of fuzzy inferenc...