Abstract
AbstractIn this paper, a stochastic computational intelligence approach for solution of fractional differential equations has been used. In this method, the strength of feed forward artificial neural networks is used to accurately model the equation and Genetic algorithm applied for learning of weights aided by active set algorithm for rapid local search. The design scheme has been successfully applied to solve different types of linear and nonlinear ordinary fractional differential equations. The results were compared with exact solutions, approximate analytic solution and standard numerical techniques. In case of linear ordinary fractional differential equations, relatively more accurate solutions were obtained than standard numerical methods. However, for complex non-linear fractional differential equation, the same scheme is applicable, but with reduced accuracy. The advantage of this approach is that it provides the solution on continuous entire finite domain unlike the other numerical techniques.KeywordsComputational intelligenceFractional differential equationsGenetic algorithmsNeural NetworksNumerical Computing
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