The properties of time series generated by a perceptron with monotonic and non-monotonic transfer function, where the next input vector is determined from past output values, are examined. The analysis of the parameter space reveals the following main finding: a perceptron with a monotonic function can produce fragile chaos only, whereas a non-monotonic function can generate robust chaos as well. For non-monotonic functions, the dimension of the attractor can be controlled monotonically by tuning a natural parameter in the model.
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Priel et al. (2000) studied this question.
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