Abstract – This paper investigates the identification of nonlinear systems by neural networks. As the identification methods, Feedforward Neural Networks (FNN), Radial Basis Function Neural Networks (RBFNN), Runge-Kutta Neural Networks (RKNN) and Adaptive Neuro Fuzzy Inference Systems (ANFIS) based identification mechanisms are studied and their performances are comparatively evaluated on a thr...

In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed to cancel the electrocardiogram (ECG) interference in electromyogram (EMG) signal. Conventional filtering techniques are not suitable due to an overlap in spectral content of the desired signal (EMG) and the interference. Comparison of results is made between the proposed technique and the other artificial intelligence tec...

The aim of this study was to demonstrate the effectiveness of an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of diesel spray penetration length in the cylinder of a diesel internal combustion engine. The technique involved extraction of necessary representative features from a collection of raw image data. A comparative evaluation of two fuzzy-derived techniques for modelli...

Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rulebase optimization methods to perform efficiently whe...

The fault diagnosis of the dissolved gas analysis (DGA) of the power transformer is to be enhanced than previous adopted techniques; this paper proposes a novel adaptive neuro Fuzzy inference system for the incipient fault recognition through enhanced approach. Complying with the practical DGA records and associated fault causes as much as possible, an ANFIS algorithm is presented to establish ...

Abstract This study presents the development, analysis and assessment of residential lighting load profile using computational intelligence based modelling Adaptive Neuro Fuzzy Inference System (ANFIS) and Neural network (NN) models for prediction (forecasting) and evaluation of lighting load and initiatives. Factors considered in the development of the models include natural lighting, occupanc...

This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) for financial trading, which learns to predict price movements from training data consisting of intraday tick data sampled at high frequency. The empirical data used in our investigation are five-minute mid-price time series from FX markets. The ANFIS optimisation involves back-testing as well as varying the number of epochs...

Worms spread by scanning for vulnerable hosts across the Internet. In this paper we report a comparative study of three classification schemes for automated portscan detection. These schemes include a simple Fuzzy Inference System (FIS) that uses classical inductive learning, a Neural Network that uses back propagation algorithm and an Adaptive Neuro Fuzzy Inference System (ANFIS) that also emp...

Solubility of carbon dioxide in poly(vinyl acetate) (PVAc), poly(2,6-dimethyl-1,4-phenylene ether) (PPO), polypropylene (PP), and high-density polyethylene (HDPE), poly(butylene succinate) (PBS), poly(butylene succinate-co-adipate) (PBSA) and polystyrene (PS) are modeled by adaptive neuro-fuzzy inference system (ANFIS) in wide range of pressure and temperature. The results obtained in this work...

A new hybrid filtering technique is proposed to improving denoising process on digital images. This technique is performed in two steps. In the first step, uniform noise and impulse noise is eliminated using decision based algorithm (DBA). Image denoising process is further improved by an appropriately combining DBA with Adaptive Neuro Fuzzy Inference System (ANFIS) at the removal of uniform no...