the main purpose of this paper is prediction of tse corporate financial bankruptcy using artificial neural networks. the mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (working capital to total assets, net income to total assets, total debt to total assets, current assets to current liabilities, quick assets to current liabilities). the neu...

this paper intends to offer a new iterative method based on arti cial neural networks for finding solution of a fuzzy equations system. our proposed fuzzi ed neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of arti cial neural networks, can get a real input vector and calculates its corresponding fu...

the application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. a radial basis function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. in the proposed model, the trained neural network represents the kinetics of biological decomposition of organic matters in the reactor. the neural network has b...

in this paper, a novel hybrid model based on neural network and game theory is proposed to support the analyzers in oil market. in this model, first the neural network is utilized to learn the oil prices associated with opec production level and usa imports level. then the learned neural network is applied by a game model. finally the nash equilibrium points of the game present the optimum deci...

this paper deals with volume fraction optimization of functionally graded (fg) beams resting on elastic foundation for maximizing the first natural frequency. the two-constituent functionally graded beam consists of ceramic and metal. these constituents are graded through the thickness of beam according to a generalized power-law distribution. one of the advantages of using generalized power- l...

analysis of structures for earthquake induced loads is very expensive in terms of the computational burden. In this study, to reduce the computational effort a new neural system which is called parallel wavelet back propagation (PWBP) neural networks has been introduced. Training of PWBP is implemented in two phases. In the first phase, the input space is classified by using competitive neural ...

analysis of structures for earthquake induced loads is very expensive in terms of the computational burden. In this study, to reduce the computational effort a new neural system which is called parallel wavelet back propagation (PWBP) neural networks has been introduced. Training of PWBP is implemented in two phases. In the first phase, the input space is classified by using competitive neural ...

This paper considers the use of discrete Hopfield neural networks for solving school timetabling problems. Two alternative formulations are provided for the problem: a standard Hopfield-Tank approach, and a more compact formulation which allows the Hopfield network to be competitive with swapping heuristics. It is demonstrated how these formulations can lead to different results. The Hopfield n...

Struggling in world's competitive markets, industries are attempting to upgrade their technologies aiming at improving the quality and minimizing the waste and cutting the price. Industry tries to develop their technology in order to improve quality via proactive quality control. This paper studies the possible paint quality in order to reduce the defects through neural network techniques in au...