The relationship between support vector machines (SVMs) and Takagi–Sugeno–Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for every used kernel function. Restricted methods to extract rules from SVMs have been previously published. Their limitations are surpassed with the presented extraction method. The behavior of SVMs is explained by means of ...

This work proposes the application of Multi-Objective Genetic Algorithms to obtain Fuzzy Rule-Based Systems with a better trade-off between interpretability and accuracy in linguistic fuzzy modelling problems. To do that, we present a new post-processing method that by considering selection of rules together with tuning of membership functions gets solutions only in the Pareto zone with the hig...

Recently, a new linguistic rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on the linguistic 2-tuples representation model, that allows the symbolic translation of a label considering an unique parameter. It involves a reduction of the search space that eases the derivation of optimal models. This work presents a new symbolic repr...

-A revolutionary innovation in the field of navigation aids, help pilots to have a smooth and safe landing. Landing depends upon the two factors: elevator deflection and throttle deflection; the relation between the two should be maintained in order to acquire a non-oscillatory output response. Membership function are developed using the crisp values of velocity and height of an aircraft. Fuzzi...

In fuzzy models, redundancy may be present in the form of similar fuzzy sets, especially in the construction of a fuzzy system from a set of given training examples. In this paper, a simple formula for calculating the degree of similarity of Trapezoidal membership functions is derived in order to merge similar membership functions and hence to obtain a more transparent rule-base with a minimum ...

Machine learning algorithms perform differently in settings with varying levels of training set mislabeling noise. Therefore, the choice of a good algorithm for a particular learning problem is crucial. In this paper, we introduce the “Sigmoid Rule” Framework focusing on the description of classifier behavior in noisy settings. The framework uses an existing model of the expected performance of...

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on mathematical PV module, which solar cell employing one-diode equivalent circuit. output current and power characteristics curves highly depend some climatic factors such as radiation temperature, are obtained by s...

The elicitation method of the fuzzy membership function depends on the interpretation of the membership function. This paper applies a recently developed neural network framework, Plausible Neural Network (PNN), to generate fuzzy membership functions automatically with or without class labeling based on the similarity and likelihood measurement. The approach combining supervised and unsupervise...