A fuzzy logic controller with interval-valued inference mechanism is presented for the control of distributed parameter system (DPS). Interval-valued fuzzy set, a special case of type-2 fuzzy set, is innovatively designed to cope with the spatial information of distributed parameter systems. The proposed fuzzy logic controller is composed of six modules: fuzzifier, fuzzy composition, rules, inf...

Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...

An inference method based on an equation image of a rule-base and the minimization of a cost index (inference error) is presented. The method has several advantages: it has intuitive insight, allows more powerful rules, and also presents an interesting algebraic (minimum distance) and functional (subsethood) formulation of the meaning of inference. Analysis of internal quality and completeness ...

In this paper, we present a novel technique to analysis fingerprint image quality using fuzzy logic. The quality of fingerprint image greatly affects the performance of minutiae extraction and the process of matching in fingerprint identification system. The system uses the extracted four features from a fingerprint image which are the Local Clarity Score (LCS), Global Clarity Score (GCS), Ridg...

The presented control scheme utilizes Adaptive Neuro Fuzzy Inference System (ANFIS) controller to track a reference engine rotational speed and disturbance rejection during engine idling. To evaluate the performance of the controller a model of the engine is simulated and simulation results presented. ANFIS implements a first order Sugeno-style fuzzy system. It is a method for tuning an existin...

The two most important models of inferencing in approximate reasoning with fuzzy sets are Zadeh’s Compositional Rule of Inference (CRI) and Similarity Based Reasoning (SBR). It is known that inferencing in the above models is resource consuming (both memory and time), since these schemes often consist of discretisation of the input and output spaces followed by computations in each point. Also ...

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-MamdaniLarsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are...

The design and analysis of seismically excited structural control systems should be based on the best available knowledge and information instead of the simplest available model when handling uncertainties in the civil structural system. Therefore, seismic structural control system is developed using fuzzy logic due to its capacity to formalize approximate reasoning processes, i.e., a knowledge...

Inference mechanisms and interpretations of fuzzy rule bases are studied together from the point of view of systems of fuzzy relation equations. A proper use of an inference mechanism connected to a fuzzy relation interpreting a fuzzy rule base is certified by keeping the fundamental interpolation condition. The paper aims at new solutions of systems of fuzzy relation equations which are motiva...

nowadays, the customer's credit assessment is one of most important challenges for managers in decision making. especially in the current situation which the number scam has increased and outstanding claims of business company is increasing. on the other hand decision making about customers and their credit is based on expert judgment and also the customer information is often uncertain, a...