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Cellular automata
A Cellular Automata can be viewed as an autonomous Finite State Machine[FSM] consisting of a number of cells.
A Cellular Automaton consists of a regular grid of cells, each as a finite number of states such as On and Off.An initial state [time t=0] is selected by assigning a state for each cell. The rule for updating the state of cells is the same for each cell and does not change over time.
Cellular Automata can also be viewed as a simple model of a spatially extended decentralized system made up of a number of individual components[cells].Cellular Automata comes in different shapes and varieties.One of the fundamental properties of a cellular automaton is the type of grid on which it is computed. The number of colors (or distinct states) a cellular automaton may assume must also be specified. This number is generally an integer, with (binary) being the simplest choice. For a binary automaton,"white" is called for color 0 and "black" for color 1. However, cellular automata having a continuous range of possible values may also be considered.
Classification
Wolfram,defined four classes into which cellular automata and several other simple computational models can be divided depending on their behavior.In order of complexity, the classes are:-
• Class I CAs evolve4 to a uniform conﬁguration of cell states, from nearly any initial conﬁguration. This state can be thought of in dynamical systems terms as a ‘point attractor’, or ‘limit point’. As one would suspect, the rules for class I CAs map from most or all possible neighbour conﬁgurations to the same new state. Initial lattice conﬁgurations do exist for some class I CAs that lead to non-trivial cycles, but these are very rare.
• CAs in Class II evolve to pro...
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...hborhood, additive CA are ideally suited for V LSI implementation. Different applications ranging from V LSI test domains to the design of a hardwired version of different CA based schemes have been proposed.
Pattern Recognition
The advent of neural net with the seminal work of Hopfield , popularized the use of machine intelligence techniques in the pattern recognition. However, the dense and inherent structure of neural networks is not suitable for VLSI implementation. So, researchers in the neural network domain tried to simplify the structure of the neural network by pruning unnecessary connections. Simultaneously, the CA research community explored the advantages of the sparse network structure of cellular automata for relevant applications. The hybridization of cellularity and neural network has given rise to the popular concept of cellular neural networks.

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