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...

We use artificial neural networks to perform curve prediction. For that, we have created a class of neural networks (feed forward multilayer perceptron networks with backpropagation) that have a topology which is determined by their genetic makeup. Using a simple evolutionary strategy on their genes, we optimise the networks’ topologies to solve the problems at hand. Using this approach, we cou...

This paper proposes an approach to construct a better Semantic Perceptron Net (SPN) used for topic spotting. To accomplish this task a learning paradigm call: neural network ensembling is used. Applying this technique to the original structure of Semantic Perceptron Net a new system called GA-SPN (Genetic Algorithm based Semantic Perceptron Net) was developed. The new system uses a neural netwo...

This paper applies Bayesian neural networks on the inference of diesel 85% ASTM distillation, and compares the results with traditional multi-layer perceptrons.

Since the seminal works of Engle [7] and Bollerslev [3] about heteroskedastic return series models, many extensions of their (G)ARCH models have been proposed in the literature. In particular, the functional dependence of conditional variances and the shape of the conditional distribution of returns have been varied in several ways (see [1] and [5] for an extensive overview). These two issues h...

As Wireless Sensor Networks are penetrating into the industrial domain, many research opportunities are emerging. One such essential and challenging application is that of node localization. A feed-forward neural network based methodology is adopted in this paper. The Received Signal Strength Indicator (RSSI) values of the anchor node beacons are used. The number of anchor nodes and their confi...

accurate estimation of hydrocarbon volume in a reservoir is important due to future development and investment on that reservoir. estimation of oil and gas reservoirs continues from exploration to end of reservoir time life and is usual upstream engineer’s involvements. in this study we tried to make reservoir properties models (porosity and water saturation) and estimate reservoir volume hydro...

In regression neural networks for pattern recognition of preprocessed guided waves signals in beams, a trained network produced large errors when identifying a test pattern not found in the training set. To improve the accuracy of results, a new neural network procedure was introduced where progressive training was performed in a series combined network with the integration of a weight-range se...

estimating of forest equipment productivity is an important aspect of managing cost in forestry, which leads to reduction of operations expenses. in other words, high capital cost in forest harvesting, is a good reason to argue forest engineering research and time modeling. this paper applied one of the artificial intelligence subsets, which are called artificial neural networks (anns), to pred...

This paper concerns the use of neural networks for predicting the residual life of machines and components. In addition, the advantage of using condition-monitoring data to enhance the predictive capability of these neural networks was also investigated. A number of neural network variations were trained and tested with the data of two different reliability-related datasets. The first dataset r...