The purpose of this study is to model and optimize the detection of tar in cigarettes during the manufacturing process and show that low yield cigarettes contain similar levels of nicotine as compared to high yield cigarettes while B (Benzene), T(toluene) and X (xylene) (BTX) levels increase with increasing tar yields. A neuro-fuzzy system which comprises a fuzzy inference structure is used to ...

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...

A neuro-fuzzy system specially suited for efficient implementations is presented. The system is of the same type as the well-known “adaptive network-based fuzzy inference system” (ANFIS) method. However, different restrictions are applied to the system that considerably reduce the complexity of the inference mechanism. Hence, efficient implementations can be developed. Some experiments are pres...

This paper reviews the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for vector-controlled induction motor drives. While conventional schemes do not deal well with the highly nonlinear nature of motor control, fuzzy logic with its adjustability and neural networks with their adaptability have been shown to be excellent alternatives. ANFIS combines the advantages of fuzzy logic and neura...

Please cite this article in press as: Talei, A., et a Expert Systems with Applications (2010), doi:10.1 Intelligent computing tools based on fuzzy logic and Artificial Neural Networks (ANN) have been successfully applied in various problems with superior performances. A new approach of combining these two powerful AI tools, known as neuro-fuzzy systems, has increasingly attracted scientists in ...

Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Back-propagation gradient descent method was performed to train the ANFIS system. The performance of the...

Now a day we have various intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proving to be dexterous when applied to a range of problems. In this paper we applied the ANFIS (Adaptive Neuro-Fuzzy Interface System) tool for detecting the normal and abnormal signal. Here the designed ANFIS model contained both approaches the neural network adaptive p...

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...

short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . these results made the hybrid tools and approaches a more common method for ...

The objective of this work is to model simulation data a dust devils in Comsol using neuro-fuzzy methods (ANFIS: Adaptive Neuro Fuzzy Inference Systems) and perceptron neural networks. Since the number simulations performed was insufficient, we used Spline function increase amount data. results show that more effective than obtained models are excellent, with Nash -Sutcliffe criterion value abo...