Alessandro Pannicelli

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In this paper we show different evolutionary algorithms in order to optimise on-line weights of feed-forward neural networks when applied to short term (20 min.) urban traffic prediction. We compare the evolutionary methods with the classical back-propagation algorithm and we show results when weights are off-line and on-line evolved. Preliminary results(More)
There is epidemiological evidence for increased non-cancer mortality, primarily due to circulatory diseases after radiation exposure above 0.5 Sv. We evaluated the effects of chronic low-dose rate versus acute exposures in a murine model of spontaneous atherogenesis. Female ApoE-/- mice (60 days) were chronically irradiated for 300 days with gamma rays at(More)
This paper describes an application of fuzzy-logic and evolutionary computation to the optimization of the start-up phase of a combined cycle power plant. We modelled process experts’ knowledge with fuzzy sets over the process variables in order to get the needed cost function for the Genetic Algorithm (GA) we used to obtain the optimal regulations. Due to(More)
We present a novel model of crowd motion and swarm behaviour, in the form of a multi-agent system. We introduce a set of behavioural rules arising from an individual’s presence within a swarm of other individuals. First, the separation force causes every agent to maintain a comfortable distance from all others in the same swarm. Second, the alignment force(More)
The ideas proposed in this work are aimed to develop a novel approach based on artificial life (alife) environments for on-line adaptive optimisation of dynamical systems. The basic features of the proposed approach are: no intensive modelling (continuos learning directly from measurements) and capability to follow the system evolution (adaptation to(More)
The ideas proposed in this work are aimed to describe a novel approach based on artificial life (alife) environments for on-line adaptive optimisation of dynamical systems. The basic features of the proposed approach are: no intensive modelling (continuous learning directly from measurements) and capability to follow the system evolution (adaptation to(More)