Process variables vary with time in certain applications. Monitoring systems let us avoid severe economic losses resulting from unexpected electric system failures by improving the system reliability and maintainability The installation and maintenance of such monitoring systems is easy when it is implemented using wireless techniques. ZigBee protocol, that is a wireless technology developed as...

in this study, application of adaptive neuro-fuzzy inference system (anfis) in forecasting three perspectives (1, 2, and 4 years) ahead of iran’s agricultural products export was compared with arima as the most common econometrics linear forecasting method. for this purpose, iran’s agricultural products export revenues related to 1959-2010, and forecast performance measures such as r2, mad, and...

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 ...

A novel adaptive network fuzzy inference system (ANFIS) based filter is presented for the enhancement of images corrupted by random valued impulse noise (RVIN). This technique is performed in two steps. In the first step, impulse noise using an Asymmetric Trimmed Median Filter (ATMF). In the second step, image restoration is obtained by an appropriately combining ATMF with ANFIS at the removal ...

In this paper, model predictive control (MPC) based on an adaptive neural-fuzzy inference system (ANFIS) is proposed to realize of omni-directional service robot in path tracking. The weight the cost function a traditional MPC needs be manually adjusted, and it difficult adjust satisfactory value. order improve performance accuracy MPC, fuzzy trained by ANFIS used adaptively MPC’s reduce error ...

A new systematic algorithm to build adaptive linguistic fuzzy models directly from input-output data is presented in this paper. Based on clustering and projection in the input and output spaces, significant inputs are selected, the number of clusters is determined, rules are generated automatically, and a linguistic fuzzy model is constructed. Then, using a simplified fuzzy reasoning mechanism...

Please cite this article in press as: Talei, A., et a Expert Systems with Applications (2010), doi:10.1 Intelligent computing tools based on fuzzy logic and Artificial Neural Networks (ANN) have been successfully applied in various problems with superior performances. A new approach of combining these two powerful AI tools, known as neuro-fuzzy systems, has increasingly attracted scientists in ...

The aim of this paper is to detect bank credit cards related frauds. The large amount of data and their similarity lead to a time consuming and low accurate separation of healthy and unhealthy samples behavior, by using traditional classifications. Therefore in this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in order to reach a more efficient and accurate algorithm. By com...

Abstract Data-driven models can predict, estimate, and monitor any highly nonlinear multi-variable behaviour of high-temperature superconducting (HTS) materials, devices to analyse their characteristics with a very high accuracy in an almost real-time procedure, which is significant figure merit as compared traditional numerical approaches. The electromechanical twisted HTS tapes under differen...

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...