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...

Neural and neuro-fuzzy models are powerful nonlinear modelling tools. Different structures, with different properties, are widely used to capture static or dynamical nonlinear mappings. Static (non-recurrent) models share a common structure: a nonlinear stage, followed by a linear mapping. In this paper, the separability of linear and nonlinear parameters is exploited for completely supervised ...

In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networks to mimic t...

slope stability analysis is an enduring research topic in the engineering and academic sectors. accurate prediction of the factor of safety (fos) of slopes, their stability, and their performance is not an easy task. in this work, the adaptive neuro-fuzzy inference system (anfis) was utilized to build an estimation model for the prediction of fos. three anfis models were implemented including g...

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...

Three Neuro-Fuzzy Inference Systems (ANFIS) including Grid Partitioning (GP), Subtractive Clustering (SCM) and Fuzzy C-means clustering Methods (FCM) have been used to predict the groutability of granular soil samples with cement-based grouts. Laboratory data from related available in litterature was used for the tests. Several parameters were taken into account in the proposed models: water:ce...

during pre-project planning as an essential phase of a project, fundamental decisions that lead to project success or failure will make. this phase of a project is more important essentially in oil, gas and petrochemical mega projects that tremendous amount of resources should consume. uncertainty in the initial phases of the project is at the highest level and therefore major project decisions...

— In this paper a new approach to model climatic variations in the Plio-Pleistocene is presented. In a recent reference, Rial in [1] introduced the working hypothesis that frequency modulation (FM) of the orbital eccentricity forcing may be an important source of the nonlinearities observed in the δO time series from deep-sea sediment cores. Two models are proposed based on the ANFIS (Adaptive ...

Fuzzy data analysis as we interpret it in this paper is the application of fuzzy systems to the analysis of crisp data. In this area, neuro-fuzzy systems play a very prominent role and are applied to a variety of data analysis problems like classification, function approximation or time series prediction. Fuzzy data analysis in general and neuro-fuzzy methods in particular make it easy to strik...