To accurately estimate the state of charge of a lithium-ion battery pack used in electric vehicles, a neurofuzzy system is proposed. The subtractive clustering is used to determine the structure and the initial parameters of the neuro-fuzzy system to reduce heuristic errors. The algorithm of adaptive neuro-fuzzy inference (ANFIS) is adopted to optimize the parameters of the neuro-fuzzy system. ...

Neuro{fuzzy combination are considered for several years already. However, the term \neuro{fuzzy" still lacks of proper deenition, and it has the avor of a \buzz word". In this paper we try to give it a meaning in the context of fuzzy classiication systems. From our point of view \neuro{fuzzy" means the employment of heuristic learning strategies derived from the domain of neural network theory...

An intelligent learning-based approach using neural network and fuzzy logic to the problem of interference canceling is proposed in the paper. The famous signal-processing structure of adaptive noise canceling is used for the research of interference signal canceling, in which a neuro-fuzzy system is used as the adaptive notch filter. Four T-S fuzzy rules are in the neuro-fuzzy filter. The filt...

This study investigated the prediction of suspended sediment load in a gauging station in the USA by neuro-fuzzy, conjunction of wavelet analysis and neuro-fuzzy as well as conventional sediment rating curve models. In the proposed wavelet analysis and neuro-fuzzy model, observed time series of river discharge and suspended sediment load were decomposed at different scales by wavelet analysis. ...

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...

unknown noise and artifacts present in medical signals with  non-linear fuzzy filter will be estimate and then removed. an adaptive neuro-fuzzy interference system which has a nonlinear  structure presented  for the noise function prediction by before samples. this paper is about a neuro-fuzzy method to estimate unknown noise of electrocardiogram (ecg) signal. adaptive neural combined with fuzz...

The paper presents a short review how to use feedforward neural networks for non-linear system identification, with application at the neural implementation of a fuzzy system. In this application the inputoutput transfer characteristics of the fuzzy system are used to evaluate the accuracy of the identification results expressed for a neuro-fuzzy model. This method could be used for identificat...

The goal of this work was to develop and validate a neuro-fuzzy intelligent system (LOLIMOT) for rectal temperature prediction of broiler chickens. The neuro-fuzzy network was developed using SCILAB 4.1, on the ground of three Departamento de Engenharia, Universidade Federal de Lavras (UFLA), Caixa Postal 3037, Lavras/MG, Brasil [email protected] [email protected] [email protected] Des...

Neuro-Fuzzy Modeling has been applied in a wide variety of fields such as Decision Making, Engineering and Management Sciences etc. In particular, applications of this Modeling technique in Decision Making by involving complex Systems of Linear Algebraic Equations have remarkable significance. In this Paper, we present Polak-Ribiere Conjugate Gradient based Neural Network with Fuzzy rules to so...

A new complex neuro-fuzzy self-learning approach to the problem of function approximation is proposed, where complex fuzzy sets are used to design a complex neuro-fuzzy system as the function approximator. Particle swarm optimization (PSO) algorithm and recursive least square estimator (RLSE) algorithm are used in hybrid way to adjust the free parameters of the proposed complex neuro-fuzzy syst...