-Now a day we have various types of intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proving to be skillful when applied to a different kind of problems. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. here we applied tool for detecting the two dif...

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In a...

In this paper an effort has been made to design and demonstrate the use of fuzzy logic based model to predict the tensile strength of tubular joints of GI pipes which are welded with the technique of radial friction welding. The model is based on two inputs signals; rotational speed (RPM) and forge load. The Adaptive Neuro-Fuzzy Inference System (ANFIS) technique of fuzzy based systems for mode...

The control of walking robots has improved dramatically over the last few years. Several methodologies have been developed for this. As one of them, in this work, we used fuzzy logic controller that generates angular of the knees in order to active a desired position of the center of mass. We present a fuzzy gait generator, FGG, that generates the angular trajectories of a biped robot. The FGG,...

It is known that neuro-fuzzy system is easily stuck in local minimum. To improve these drawbacks, a two-stage algorithm combining the advantages of neuro-fuzzy and genetic algorithms (GA) is integrated in system identification. Genetic algorithms are general purposed optimization algorithms with adaptive reproduction, crossover, and mutation operators that provide a method to search optimal par...

In this paper, the idea of the neuro-fuzzy learning algorithm has been extended, by which the tuning parameters in the fuzzy rules can be learned without changing the fuzzy rule table form used in usual fuzzy applications. A new neuro-fuzzy learning algorithm in the case of the fuzzy singleton-type reasoning method has been proposed. Due to the flexibility of the fuzzy singleton-type reasoning ...

Quality of Service (QoS) support in Mobile Ad hoc NETworks (MANETs) is a challenging task due to bandwidth and delay constraints, varying channel conditions, power limitations, node mobility and dynamic topology. This paper proposes an intelligent agent based ondemand (source initiated) delay aware QoS routing scheme in MANETs by using software agents that employ neuro-fuzzy logic supported by ...

This paper reviews neuro-fuzzy systems, which combine methods from neural network theory with fuzzy systems. Such combinations have been considered for several years already. However, the term neuro-fuzzy still lacks proper deenition, and still has the avour of a buzzword to it. Surprisingly few neuro-fuzzy approaches do actually employ neural networks, even though they are very often depicted ...

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 ...

The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and ar...