The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used ...

Bio-absorbent palm fiber was applied for removal of cationic violet methyl dye from water solution. For this purpose, a solid phase extraction method combined with the artificial neural network (ANN) was used for preconcentration and determination of removal level of violet methyl dye. This method is influenced by factors such as pH, the contact time, the rotation speed, and the adsorbent dosag...

An artificial neural network model trained by using various learning algorithms is designed to classify the electrocardiographic signals in this study. The of constructed on structure consisting a multilayered perceptron based feed forward back propagation. A data pool built dataset consists 66 data’s taken from MIT BIH arrhythmia database perform training and testing processes model. process p...

We propose a method for learning in multilayer perceptrons (MLPs). It includes new self-adapting features that make it suitable for dealing with a variety of problems without the need for parameter re-adjustments. The validity of our approach is benchmarked for two types of problems. The first benchmark is performed for the topologically complex parity problem with a number ofbinary inputs rang...

In this work, response surface methodology (RSM) and artificial neural network (ANN) were used to predict the decolorization efficiency of Reactive Red 33 (RR 33) by applying the O3/UV process in a bubble column reactor. The effects of four independent variables including time (20-60 min), superficial gas velocity (0.06-0.18 cm/s), initial concentration of dye (50-150 ppm), and pH (3-11) were i...

The computer gaming industry is large and growing rapidly. Consumers demand realistic motion in computer games. Consequently, the ability to realistically simulate motion is vital in computer games development. Currently, most simulation of motion is done through the use of a physics engine which essentially involves numerically solution of the set of differential equations which describe the c...

We present deterministic nonmonotone learning strategies for multilayer perceptrons (MLPs), i.e., deterministic training algorithms in which error function values are allowed to increase at some epochs. To this end, we argue that the current error function value must satisfy a nonmonotone criterion with respect to the maximum error function value of the M previous epochs, and we propose a subpr...

Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...

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...