Now a day we have various intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proving to be dexterous when applied to a range of problems. In this paper we applied the ANFIS (Adaptive Neuro-Fuzzy Interface System) tool for detecting the normal and abnormal signal. Here the designed ANFIS model contained both approaches the neural network adaptive p...

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...

Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Backpropagation gradient descent method was performed to train the ANFIS system. The performance of the ...

Conventional control algorithms used in pH control systems give inefficient performance, leading to use of large mixers. To improve the neutralization control process, an ANFIS based advanced controller has been proposed. In this paper, method of design of adaptive controller based on neurofuzzy technique is presented. The method uses ANFIS methodology to automatically generate fuzzy rule base ...

In this article, a new Runge-Kutta-Nyström method is derived. The new RKN method has zero phase-lag, zero amplification error and zero first derivative of phase-lag. This method is basically based on the sixth algebraic order Runge-Kutta-Nyström method, which has proposed by Dormand, El-Mikkawy and Prince. Numerical illustrations show that the new proposed method is much efficient as compared w...

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy infer...

Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Back-propagation gradient descent method was performed to train the ANFIS system. The performance of the...

In this study, an efficient method is introduced to predict the stability of soil-structure interaction (SSI) system subject to earthquake loads. In the procedure of the nonlinear dynamic analysis, a number of structures collapse and then lose their stability. The prediction of failure probability is considered as stability criterion. In order to achieve this purpose, a modified adaptive neuro ...

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...