A Proportional-Integral (PI) controller and a non-linear Fuzzy and Neuro controller is designed and its application to the regulation of 54V, 2.916kW Quasi-Resonant Buck Converter is comparatively investigated. PI controller involves Proportional gain (Kp) and Integral time (Ki) parameters whose manual tuning provides an appropriate action. Fuzzy logic is on the notion of graded membership and ...

An adaptive neuro-fuzzy inference system (ANFIS) is implemented to evaluate traffic noise under heterogeneous traffic conditions of Nagpur city, India. The major factors which affect the traffic noise are traffic flow, vehicle speed and honking. These factors are considered as input parameters to ANFIS model for traffic noise estimation. The proposed ANFIS model has implemented for traffic nois...

The aim of the paper is to demonstrate the neuro-fuzzy support of knowledge management in social regulation. Knowledge, defined as human capability of making data and information useful for decision making processes, could be understood for social regulation purposes as explicit and tacit. Explicit knowledge relates to the community culture indicating how things work in the community based on s...

This paper describes a self-tuning Neuro-fuzzy Controller by Genetic Algorithm (NFCGA) applied to a coupled-tank fluid level control. The controller use a simplified fuzzy algorithm which based on the Radial Basis Function neural network (RBF). Genetic Algorithm (GA) is used to tune simultaneously all the parameters of controller from a random state. A plant model is used initially to tune the ...

A fuzzy logic and neuro-fuzzy system for classification of hydrometeor type based on polarimetric radar measurements is described in this paper. The hydrometeor classification system is implemented by using fuzzy logic and a neural network, where the fuzzy logic is used to infer hydrometeor type, and the neural network learning algorithm is used for automatic adjustment of the parameters of the...

This paper proposes the implementation of a very simple but efficient Adaptive Neuro-Fuzzy Inference System ( ANFIS )based algorithm to detect the edges of an gray scale image. The proposed approach begins by scanning the images using floating 3x3 pixel window. ANFIS system designed has 8 inputs, which corresponds to 8 pixels of instantaneous scanning matrix, one output that tells whether the p...

OBJECTIVE To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. METHOD A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning algorithm (ANNB). RESULTS The study was composed by 244 patients with a bigger frequenc...

Estimation of distribution algorithm for optimization of neural networks for intrusion detection system p. 9 Neural network implementation in reprogrammable FPGA devices-an example for MLP p. 19 A new approach for finding an optimal solution and regularization by learning dynamic momentum p. 29 Domain dynamics in optimization tasks p. 37 Nonlinear function learning by the normalized radial basi...

Recently the number of distance education platforms has been increased significantly. These platforms provide isolation between the student and teacher. Thus, there is a need for predicting the students who are possible to fail in a specific course and take the precautions like starting face-to-face on demand lectures for individual cases. Both of artificial intelligence and data mining techniq...

The mountain car problem is a well-known task, often used for testing reinforcement learning algorithms. It is a problem with real valued state variables, which means that some kind of function approximation is required. In this paper, three reinforcement learning architectures are compared on the mountain car problem. Comparison results are presented, indicating the potentials of the actor-onl...