Solving Blasius Differential Equation by Using Hybrid Neural Network and Gravitational Search Algorithm (HNNGSA)

An integrated Neural Network and Gravitational Search Algorithm (HNNGSA) are used to solve Blasius differential equation. To aim this purpose, GSA technique is applied to train a multi-layer perceptron neural network, which is used as approximation solution of the Blasius differential equation. A trial solution of the differential equation is written as sum of two parts. The first part satisfies the initial/ boundary conditions and does contain an adjustable parameter and the second part which is constructed so as not to affect the initial/boundary conditions. The second part involves adjustable parameters (the weights and biases) for a multi-layer perceptron neural network. In order to demonstrate the presented method, the obtained results of the proposed method are compared with some numerical methods. The results of the proposed method were in good agreement with the numerical methods. Key-Words: Neural Networks (NNs), Gravitational Search Algorithm (GSA), Blasius Differential Equation, Approximation Solutions.