The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

This paper presents a feed forward back-propagation neural network model to predict the retained tensile strength and design chart in order to estimation of the strength reduction factors of nonwoven geotextiles due to installation process. A database of 34 full-scale field tests were utilized to train, validate and test the developed neural network and regression model. The results show that t...

protection systems have vital role in network reliability in short circuit mode and proper operating for relays. current transformer often in transient and saturation under short circuit mode causes mal-operation of relays which will have undesirable effects. therefore, proper and quick identification of current transformer saturation is so important. in this paper, an artificial neural network...

estimating the final price of products is of great importance. for manufacturing companies proposing a final price is only possible after the design process over. these companies propose an approximate initial price of the required products to the customers for which some of time and money is required. here using the existing data of already designed transformers and utilizing the bayesian anal...

This paper reports the effect of the step-size (learning rate parameter) on the performance of the backpropgation algorithm. Backpropagation algorithm (BP) is used to train multilayer neural network. BP algorithm is the generalized form of the least mean square (LMS) algorithm. In this proposed backpropagation algorithm different learning rate parameter are used in different layer. The learning...

Estimation (Forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. Thus, accuracy of the estimation is highly desirable. Hibrid Regression Neural Network is an approach proposed in this paper to obtain better fitness in comparison with Regression Analysis and the Neural Network methods. Comparing the estimated resul...

in recent years, the existing competitions between investment companies have been increased largely by entering private investors in capital market. large and powerful companies try to achieve the goals predicted to increase the competition capacity. to analyze the efficiency of investment companies, parametric and non-parametric methods are used. in this research, based on the dissociation pow...

The current paper presents a robust optimum design of friction stir welding (FSW) lap joint AA1100 aluminum alloy sheets using Monte Carlo simulation, NSGA-II and neural network. First, to find the relation between the inputs and outputs a perceptron neural network model was obtained. In this way, results of thirty friction stir welding tests are used for training and testing the neural network...

results survival probabilities at different times were determined using the cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the kaplan-meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between cox and kaplan-meier (p < 0.05) and no significant difference between cox and...