This paper presents the architecture and learning procedure underlying ANFIS (Adaptive-Network-based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data ...

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...

The steady-state operation of maintaining voltage stability is done by switching various controllers scattered all over the power network. When a contingency occurs, whether forced or unforced, the dispatcher is to alleviate the problem in a minimum time, cost, and effort. Persistent problem may lead to blackout. The dispatcher is to have the appropriate switching of controllers in terms of typ...

In this paper a pose invariant face recognition using neuro-fuzzy approach is proposed. Here adaptive neuro fuzzy interface system (ANFIS) classifier is used as neuro-fuzzy approach for pose invariant face recognition. In the proposed approach the preprocessing of image is done by using adaptive median filter. It removes the salt pepper noise from the original images. From these denoised images...

Accurate segmentation of magnetic resonance (MR) images of the brain is of interest in the study of many brain disorders. In this paper, we provide a review of some of the current approaches in the tissue segmentation of MR brain images. We broadly divided current MR brain image segmentation algorithms into three categories: classificationbased, region-based, and contour-based, and discuss the ...

A method based on adaptive neuro-fuzzy inference system (ANFIS) for computing the effective permittivity and the characteristic impedance of the micro-coplanar strip (MCS) line is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). A hybrid learning algorithm, which combines the least square method and the backpropagation alg...

In this work a new method based on the adaptive neuro-fuzzy inference system (ANFIS) was successfully introduced to determine the characteristic parameters, effective permittivities and characteristic impedances, of conventional coplanar waveguides. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid-learning algorithm,...

Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algor...

This paper presents a comprehensive study of ANFIS+ARIMA+IT2FLS models for forecasting the weather of Raipur, Chhattisgarh, India. For developing the models, ten year data (2000-2009) comprising daily average temperature (dry-wet), air pressure, and wind-speed etc. have been used. Adaptive Network Based Fuzzy Inference System (ANFIS) and Auto Regressive Moving Average (ARIMA) models based on In...

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 ...