Properties and discrimination of chaotic maps

Properties, discrimination, and practical applications of chaotic maps of the unit interval are considered. Emphasis is placed on a specific class of piecewise linear, one-dimensional maps, members of which give rise to finite-state Markov chains. Properties of maps in this class are presented; these properties suggest the value of these maps for generating random variables and processes. Algorithms are introduced for discriminating among these maps based on noisy observations, and preliminary experimental results are presented which illustrate the behavior of these algorithms. The use of these maps and discrimination algorithms for communication applications is considered.<>