Abstract
An algebraic approach for representing multidimensional nonlinear functions by feedforward neural networks is implemented for the approximation of smooth batch data containing input-output of the hidden neurons and the final neural output of the network. The training set is associated to the adjustable parameters of the network by weight equations. Then we have obtained the exact input weight of the nonlinear equations and the approximated output weight of the linear equations using the conjugate gradient method with an adaptive learning rate. Using a multi-agents system as different kinds of energies for the plant growth, one can predict the height of the plant.
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Dagba, T.K., Adanhounmè, V., Adédjouma, S.A. (2010). Neural Networks for Solving the Superposition Problem Using Approximation Method and Adaptive Learning Rate. In: Jędrzejowicz, P., Nguyen, N.T., Howlet, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2010. Lecture Notes in Computer Science(), vol 6071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13541-5_10
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DOI: https://doi.org/10.1007/978-3-642-13541-5_10
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