In this paper river stage discharge models using Adaptive NeuroFuzzy Inference System (ANFIS) and Linear Multiple Regression (MLR) methods have been developed. This paper also investigates the best model to forecast river discharge. From the literature it is clear that ANN models and Fuzzy logic models are quite applicable on river stage discharge modelling. Hence this present study carried out...

Weight control system on the feeder conveyor determines the factor of the quality of products within an industry. The dynamics of the flow rate of material through the feeder conveyor weigh requires a good level of performance controllers. The base of current controllers such as FLC (Fuzzy Logic Controller) requires a certain amount of knowledge and expertise in its design that will make it dif...

An Adaptative Neuro-Fuzzy Inference System (ANFIS) is developed to predict the acoustic form function (FF) for an infinite length cylindrical shell excited perpendicularly to its axis. The Wigner-Ville distribution (WVD) is used like a comparison tool between the calculated FF by the analytical method and that predicted by the neuro-fuzzy technique for a copper tube. During the application of t...

this study investigates the prediction model of compressive strength of self–compacting concrete (scc) by utilizing soft computing techniques. the techniques consist of adaptive neuro–based fuzzy inference system (anfis), artificial neural network (ann) and the hybrid of particle swarm optimization with passive congregation (psopc) and anfis called psopc–anfis. their performances are comparativ...

Article history: Received 7 May 2012 Received in revised form 10 August 2012 Accepted 6 September 2012 Available online 7 November 2012

this paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. the uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. the contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...

Fuzzy inference systems have been applied in recent years in various medical fields due to their ability to obtain good results featuring white-box models. Adaptive Neuro-Fuzzy Inference System (ANFIS), which combines adaptive neural network capabilities with the fuzzy logic qualitative approach, has been previously used in modelling survival of breast cancer patients based on patient groups de...

This work is an attempt to illustrate the usage and effectiveness of soft computing techniques in the estimation and control of multi input and multi output systems. This paper focuses on neuro-fuzzy system ANFIS (Adaptive Neuro Fuzzy Inference system). An Adaptive Network based Fuzzy Interference System architecture extended to cope with multivariable systems has been used. The performance of ...

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 composition of simple local models for approximating complex nonlinear mappings is a common practice in recent modeling and control literature. This paper presents a comparative analysis of two different local approaches: the neuro-fuzzy inference system and the lazy learning approach. A neuro-fuzzy system is an hybrid representation which combines the linguistic description of fuzzy infere...