Dynamic Systems Modelling with Evolving Cellular Automata
Dynamic systems modelling with evolving cellular automata is described in this paper. The basic idea is to use stochastic cellular automata together with local evolving algorithms in order to model dynamic systems representing certain physical phenomena and on the other hand to search for the optimal local parameters that enable the close fit of the model processing with the actual sequence of the phenomena. With the help of a case study on the problem of the spread of forest fires, we show the value of the approach.
KeywordsCellular Automaton Local Parameter Cellular Automaton Learning Sequence Fire Spread
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