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...

Recently neural networks have been ploposed as new computational tools for solving constrained optimization problems. In this paper the minimum cost path fmding algorithm is proposed by using a Hopfield type neural network. In order to design a Hopfield type neural network, an energy function must be defmed at f i t . To achieve thii, the concept of a vector-represented network is used to descr...

in this paper, a novel hybrid method based on learning algorithmof fuzzy neural network and newton-cotesmethods with positive coefficient for the solution of linear fredholm integro-differential equation of the second kindwith fuzzy initial value is presented. here neural network isconsidered as a part of large field called neural computing orsoft computing. we propose alearning algorithm from ...

This paper presents an improved approach for enhancing the performance of doing logic programming in Hopfield neural network. Generally Hopfield networks are suitable for solving combinatorial optimization problems. In spite of usefulness of Hopfield neural networks they have limitations; one of the most concerning drawbacks is that sometimes the solutions are local minimum instead of global mi...

The forecast of fluctuations and prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critically favorable to the investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help to make the firm de...