Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Solving Nonlinear Differential Equations by a Neural Network Method
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Information Sciences: an International Journal
IEEE Transactions on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A MLP solver for first and second order partial differential equations
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolutionary computational intelligence in solving the fractional differential equations
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Computers & Mathematics with Applications
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A method is presented to solve partial differential equations (pde's) and its boundary and/or initial conditions by using neural networks. It uses the fact that multiple input, single output, single hidden layer feedforward networks with a linear output layer with no bias are capable of arbitrarily well approximating arbitrary functions and its derivatives, which is proven by a number of authors and well known in literature. Knowledge about the pde and its boundary and/or initial conditions is incorporated into the structures and the training sets of several neural networks. In this way we obtain networks of which some are specifically structured. To find the solution of the pde and its boundary and/or initial conditions we have to train all obtained networks simultaneously. Therefore we use an evolutionary algorithm to train the networks. We demonstrate the working of our method by applying it to two problems.