Abstract
The Neural Network (NN) model proposed in this paper has got a very strong biological analogy; in fact, the basic mechanisms of the signal transmission, particularly the frequency, among biological neurones are considered to model this kind of NN. The information on the NN synapses are transformed in a sinusoidal time-varying signal while in the traditional NN they are constant; furthermore, as in the biological case each neurone can have a proper working frequency. The complete theory of the Modulated Asynchronous Information Arrangement NN will not be presented here [14], but only the basic concepts as well as the techniques used to resolve both linear and non linear NN though the MAIA algorithms in supervised learning modality are presented.
M.N. Postorino dealt with the transport aspects, while G. Pappalardo, D. Rosaci, G.M.L. Sarnè dealt with the neural network aspects.
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Pappalardo, G., Postorino, M.N., Rosaci, D., Sarnè, G.M.L. (1998). MAIA Neural Network: An Application to the Railway Anti-Skating System. In: Marinaro, M., Tagliaferri, R. (eds) Neural Nets WIRN VIETRI-97. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1520-5_22
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