Numerical Solution of ordinary Differential Equations Based on Semi-Taylor by Neural Network improvement

Authors : Somayeh EZADİ, Sahar AKSARI, Mitra JASEMI

Pages : 2584-2589

View : 65 | Download : 15

Publication Date : 2015-05-13

Article Type : Review Paper

Abstract :Abstract. In this paper, a new approach is proposed in order to solve the differential equations of ordinary initial value based on the feed-forward neural network and Semi-Taylor series ordinary differential equation is first replaced by a system of ordinary differential equations. A trial Solution of this System consists of two parts. The first part, that is, Semi-Taylor series contains no adjustable parameters. And the second part includes the neural network and adjustable parameters (the weights). Using modified neural network requires that training points be selected over the open interval (a, b) without training the network in the range of the first and end points. Therefore, the calculating volume containing computational error is reduced. In fact, the training points depending on the distance [a, b] selected for training neural networks are converted into similar points in the open interval (a, b) using the new approach, then the network is trained in these similar areas.In comparison with existing similar neural networks, the proposed model provides solutions with high accuracy. Numerical examples with simulation results illustrate the effectiveness of the proposed model.
Keywords : Ordinary Differential Equations, Semi Taylor, MLP Neural Network, bfgs Technique

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