Junaid Ali Khan

Learn 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)
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 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)
Abstract—In this article an evolutionary technique has been used for the solution of nonlinear Riccati differential equations of fractional order. In this method, genetic algorithm is used as a tool for the competent global search method hybridized with active-set algorithm for efficient local search. The proposed method has been successfully applied to(More)
A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter(More)
A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the(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)