Synthesis of binary cellular automata based on binary neural networks

This paper studies a simple synthesis algorithm of desired cellular automata (ab. CAs) based on a binary neural network (ab. BNN). The CAs have binary state variable and the BNN has bipolar connection parameters. In order to realize dynamics of the CA, the bipolar parameters of BNN are determined using the genetic algorithm. Performing basic numerical experiments we show that the BNN can realize desired dynamics with small number of hidden layers for a class of CAs. We also consider the case of noisy teacher signals. These results provide basic information for application to signal processing, analysis of digital nonlinear phenomena, and so on.