water quality assessment provides a scientific basis for water resources development and management. this case study proposes a factor analysis- hopfield neural network model (fhnn) based on factor analysis method and hopfield neural network method. the results showed that the factor analysis (fa) technique was introduced to identify important water quality parameters. results revealed that bio...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

This paper illustrates a hybrid prediction system consists of Rough Set Theory (RST) and Artificial Neural Network (ANN) for processing medical data. In the process of developing a new data mining technique and software to aid efficient solutions for medical data analysis, we propose a hybrid tool that incorporates RST and ANN to make efficient data analysis and suggestive predictions. In the e...

Sign languages commonly serve as an alternative or complementary mode of human communication Tracking is one the most fundamental problems in computer vision, and use a long list applications such sign recognition. Despite great advances recent years, tracking remains challenging due to many factors including occlusion, scale variation, etc. The mistake detecting head left hand instead right ov...

In this paper, an artificial neural network is proposed for feature extraction of hand written characters. The learning algorithm is developed based on a proposed modified Sammon’s stress for our feedforward neural networks, which can not only minimize intra class pattern distances but also preserve interclass distances in the output feature space. The proposed feature extraction method tries t...

Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. This may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. Accordingly, the purpose of this study was to predict the financial crisis of Tehran Stock Exchange using neural network and genetic algorithm. This research is descripti...

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 ...

Objective: This paper presents a new approach for fault classification in extra high voltage (EHV) transmission line using a rough membership neural network (RMNN) classifier. Methods:Wavelet transform is used for the decomposition of measured current signals and for extraction of ten significant time–frequency domain features (TFDF), as well as three distinctive time domain features (TDF) part...

Today , high accuracy of character recognition is attainable using Neural Network for problems with relatively small number of categories. But for large categories, like Chinese characters, it is difficult to reach the neural network convergence because of the “local minima problem” and a large number of calculation. Studies are being done t o solve the problem by splitting the neural network i...