Design of a Fuzzy Logic Controller (FLC) requires more design decisions than usual, for example rule base, inference engine, defuzzification, and data preand post processing. This project is a humble effort to implement an FLC using VHDL for motor control. There are three parts to fuzzy controller, the fuzzification of the inputs, the defuzzification of the outputs, and the rule base. The contr...

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 ...

There are many papers in the literature that deal with the problem of the design of a fuzzy system from a set of given training examples. Those who get the best approximation accuracy are based on TSK fuzzy rules, which have the problem of not being as interpretable as Mamdany-type Fuzzy Systems. A question now is posed: How can the interpretability of the generated fuzzy rule-table base be inc...

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rule-based systems, i.e., evolutionary algorithm-based processes to automatically design fuzzy rulebased systems by learning andror tuning the fuzzy rule b...

“Fuzzy Functions” are proposed to be determined separately by two regression estimation models: the least squares estimation (LSE), and Support Vector Machines for Regression (SVR), techniques for the development of fuzzy system models. LSE model tries to estimate the fuzzy function parameters linearly in the original space, whereas SVR algorithm maps the data samples into higher dimensional fe...

Fuzzy Logic Controllers are knowledge-based systems, incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions. The deenition of these Fuzzy Rules and Fuzzy Membership Functions is generally aaected by subjective decisions, having a great innuence over the performance of the Fuzzy Controller. In some cases, the Membership Functions are deened wit...

This paper presents some highlights in the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition. These techniques are capable of dealing with inexact and imprecise problem domains and have been demonstrated to be useful in the solution of classification problems. It addresses the issue of the application of appropriate evaluation cri...

Fuzzy rule interpolation based reasoning methods are the most common choices for cases when the applied rule base is not dense. This paper presents a new technique called LESFRI, which is based on the method of least squares. Its central idea is the conservation of the shape type specific to a fuzzy partition. The method has low computational complexity.

fuzzy newton-cotes method for integration of fuzzy functions that was proposed by ahmady in [1]. in this paper we construct error estimate of fuzzy newton-cotes method such as fuzzy trapezoidal rule and fuzzy simpson rule by using taylor's series. the corresponding error terms are proven by two theorems. we prove that the fuzzy trapezoidal rule is accurate for fuzzy polynomial of degree one and...

From the viewpoint of Behaviour based Control many control tasks can be divided into separate behaviour components. By defining the relevant behaviour components, the actual control action can be constructed based on the individual control actions of the component behaviours. In this case the control action is either related to an individual behaviour component or to a fusion of behaviour compo...