Chains of discrete-time chaotic neural networks for generation of broadband signals with applications in improved ciphering systems

This paper presents a new approach for generating broadband noise-like signals by exploiting chaotic dynamics in small recurrent neural networks. A new neural network structure, the chain of chaotic neural networks (CCNN) is introduced in order to obtain a high degree of scrambling for the output signal. Comparing with other chaotic or pseudo-random sequence generators, this one has improved characteristics which make them very attractive for using in various applications where a deterministic but having white-noise like properties signals are needed. Fixed point implementation effects were investigated, simulation results proving that such systems may be efficiently implemented in digital technology and thus offering very well protected ciphering sequences