The major aim of processing satellite images is to prepare topical and effectivemaps. The selection of appropriate classification methods plays an important role. Amongvarious methods existing for image classification, artificial neural network method is ofhigh accuracy. In present study, TM images of 1987, and ETM+ images of 2000 and 2006were analyzed using artificial fuzzy ARTMAP neural netwo...

This paper is the second of two companion papers. The foundations of the proposed method of heuristic constraint enforcement on membership functions for knowledge extraction from a fuzzy/neural architecture was given in Part I. Part II develops methods for forming constraint sets using the constraints and techniques for finding acceptable solutions that conform to all available a priori informa...

Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In whi...

Fuzzy logic systems technology, particularly fuzzy control and fuzzy modeling techniques, is one of the most successful practical applications of fuzzy set and logic theory. In the past few years, the trend has been to combine fuzzy logic systems technology with artificial neural network technology to produce so called neural-fuzzy or fuzzy-neural systems. The objective is to take the advantage...

In this paper, the author research on electrical equipment’s fault diagnosis based on the improved support vector machine and fuzzy clustering. Combining the support vector combined fuzzy sets and neural network to carry on the fault diagnosis is a most prosperous diagnosis method. This article put forward a sample processing method using fuzzy clustering and studied the application of fuzzy co...

In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e. (PSO-ANFIS) are proposed for modeling and prediction of the propylene/propane adsorption under various conditions. Using these computational intelligence (CI) approaches, the input parameters such as adsorbent shape (S<su...

This paper pro oses a neural fuzzy connection admission control NFCAC) scheme, which combines of the neural-net, t o solve the connection admission control (CAC) problems in ATM networks. Recently, fuzzy logic systems have been successfully applied t o deal with the traffic control related probleims and provided a robust mathematical framework for dealing with "real-world" imprecision; multi-la...

In this contribution a new supervised classification model is proposed, namely the Fuzzy Evolutionary Probabilistic Neural Network (FEPNN). The proposed model incorporates a fuzzy class membership function into the recently proposed Evolutionary Probabilistic Neural Network (EPNN). EPNN employs an evolutionary algorithm, namely the Particle Swarm Optimization (PSO), for the selection of the spr...

To find an optimal path for robots in an environment that is only partially known and continuously changing is a difficult problem. This paper presents a new method for generating a collision-free near-optimal path for an autonomous mobile robot in a dynamic environment containing moving and static obstacles using neural network and fuzzy logic with genetic algorithm. The mobile robot selects a...

Abstract: Recently, supervised artificial neural networks have obtained success to reveal and provide quantitative information concerning defects in TNDE (Thermographic NonDestructive Evaluation). Supervised neural networks may converge to local minimum and their training procedure are usually long. In this study, a neuro-fuzzy approach is applied to characterize subsurface defects in TNDE. Sim...