Abstract.In this paper, the time for energy relaxation for LittleHopfield neural network using the new activation rule is shown to be better than the relaxation time using Hebbian learning. However, this should be so given the characteristics of the activation function and show through computer simulations that this is indeed so. In this paper, it has been proven that the new learning rule has a higher capacity than Hebb rule by computer simulations. section 3; logic programming on a neural network focused on the Hopfield model is described. In section 4, Hebb and the new learning rule are been discussed. This is foIIowed by section 5, where fitness landscapes are discussed. In section 6, theory implementation of the both learning rules are been discussed. Meanwhile, section 7 contains discussion regarding the results obtained from computer simulations. Finally concluding remarks regarding this work occupy the last section.

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Abstract-The increasing penetration of renewable energy sources, the demand for more energy efficient electricity production and the increase in distributed electricity generation causes a shift in the way electricity is produced and consumed. The downside of these changes in the electricity grid is that network stability and controllability become more difficult compared to the old situation. The new network has to accommodate various means of production, consumption and buffering and needs to offer control over the energy flows between these three elements.In order to offer such a control mechanism we need to know more about the individual aspects. In this paper we focus on the modelling of distributed production. Especially, we look at the use of microCHP (Combined Heat and Power) appliances in a group of houses.The problem of planning the production runs of the microCHP is modelled via an ILP formulation, both for a single house and for a group of houses.

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