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For the solution of our problem our solution is the following. This graphical model shows a little bit how our router will be represented with the different inputs like costs and other parameters like bandwidth and link load.
The inputs at the neuron level, namely the weights and the y's, will be the costs and the routers respectively. The input I will be the variable on which all our constraints will be set so that the algorithm can satisfy them. Subsequently, our desired operation will be performed which will be that of the input I on a summation of the different input properties such as the cost and the other properties of the input y which constitute our routers. From the source to the destination of the intended router, this operation will be performed along the way by the energy function. We will use a Hopfield method that uses a single activation at a time so all the routers along the way will execute the various parameters represented above until we reach the destination. At the destination of the target router we will have an energy function that contains the minimum cost (cost, bandwidth and capacity) for this path with the intermediary of an output expressed in a potential vi to which a transfer function is applied to allow the convergence towards an optimal solution. |
IEEE/ICACT20220326 Slide.16
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