Abstract
Artificial Metaplasticity learning algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. In this research, Artificial Metaplasticity on multilayer perceptron (AMMLP) is compared with regular Backpropagation by using input sets generated with different probability distributions: Gaussian, Exponential, Uniform and Rayleigh. Artificial Metaplasticity shows better results than regular Backpropagation for Gaussian and Uniform distribution while regular Backpropagation shows better results for Exponential and Rayleigh distributions.
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de Pablos Álvaro, M., Andina, D. (2013). Robustness of Artificial Metaplasticity Learning to Erroneous Input Distribution Assumptions. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_2
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DOI: https://doi.org/10.1007/978-3-642-38637-4_2
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