Abstract Electroencephagram (EEG) is the recording of electrical activity of the brain. Though it is intended to record cerebral signals,it also records the signals that are not of cerebral origin called artifacts. Artifact removal from EEG signals is essential for better diagnosis. This paper proposes a hybrid learning algorithm based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for elimin...

This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) for predicting the surface roughness in turning operation for set of given cutting parameters, namely cutting speed, feed rate and depth of cut. Two different membership functions, triangular and bell shaped, were adopted during the training process of ANFIS in order to compare the prediction accuracy of surface roughness by t...

The quantification and prediction of inter-agent dependency requirements is one of the main concerns in Agent Oriented Requirements Engineering. To evaluate exertion load of an agent within resource constraints, this work provides a comparative analysis of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). ANN is widely known due to its capability of learning the...

-Now a day we have various types of intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proving to be skillful when applied to a different kind of problems. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. here we applied tool for detecting the two dif...

This paper proposes an approach to tune an Adaptive Neuro Fuzzy Inference System (ANFIS) inverse controller using Iterative Learning Control (ILC). The control scheme consists of an ANFIS inverse model and learning control law. Direct ANFIS inverse controller may not guarantee satisfactory response due to different uncertainties associated with operating conditions and noisy training data. In t...

This paper presents a rotor position estimation technique for a 6/4 switched reluctance machine based on Adaptive Neuro fuzzy Inference System (ANFIS). This technique is applied for modelling the nonlinear rotor position of SRM using the magnetization characteristics of the machine. ANFIS has a strong nonlinear approximation ability which could be used for nonlinear modelling and its real time ...

There has been a growing interest in combining both neural network and fuzzy system, and as a result, neuro-fuzzy computing techniques have been evolved. ANFIS (adaptive network-based fuzzy inference system) model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. In this paper, a novel structure of unsupervised ANFIS is presented to solve differential e...

This paper deals with the implementation of the Adaptive neuro fuzzy inference system (ANFIS) on a Xilinx based Field Programmable Gate Array Spartan-3E. The implemented hardware is then used to efficiently calibrate the U-Tube manometer, in which the relation between the level of mercury and the Capacitance developed across the Copper Plates of the manometer is highly non-linear. This system s...

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,...