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In this article, a stochastic technique has been developed for the solution of nonlinear Riccati differential equation of fractional order. Feed-forward artificial neural network is employed for accurate mathematical modeling and learning of its weights is made with heuristic computational algorithm based on swarm intelligence. In this scheme, particle(More)
In this paper, a swarm intelligence technique, better known as Particle swarm optimization, has been used in solving the fractional differential equations. The approximate mathematical modeling has been done by employing feed-forward artificial neural networks by defining the unsupervised error. The learning of weights for such errors has been carried out(More)
A stochastic technique has been developed for the solution of fractional order system represented by Bagley-Torvik equation. The mathematical model of the equation was developed with the help of feed-forward artificial neural networks. The training of the networks was made with evolutionary computational intelligence based on genetic algorithm hybrid with(More)
The article is based on the approximate solution of a well known Lane–Emden–Fowler (LEF) equation. A trial solution of the model is formulated as an artificial feed-forward neural network containing unknown weights which are optimized in an unsupervised way. The proposed scheme is tested successfully on various test cases of initial value problems of LEF(More)
In this article, swarm intelligence approach is proposed for the solution of problems involved in Differential Equations of first order. The modeling of these problems is performed by artificial neural network that have universal approximation capabilities. A new particle swarm optimization algorithm is used to optimize the adaptive weights of neural(More)
In this paper, a heuristic computational intelligence approach has been presented for solving the differential equations of fractional order. The strength of feed forward artificial neural networks is used to mathematically model the equations and particle swarm optimization algorithm is applied for learning of weights, aided by simulating annealing(More)
In this paper, meta-heuristic intelligent approaches are developed for handling nonlinear oscillatory problems with stiff and non-stiff conditions. The mathematical modeling of these oscillators is accomplished using feed-forward artificial neural networks (ANNs) in the form of an unsupervised manner. The accuracy as well as efficiency of the model is(More)