针对混沌信号具有的随机性、宽带性、似噪声以及对初始条件的极端敏感性的特点，本文用混沌序列取代扩频通信系统中的伪随机码，在提出并实现一种混沌网络同步化法的基础上，实现了语音、文字和图像的码分多址的扩频通信。用Lorenz混沌系统对同步和通信的仿真结果表明了本文所述方法的有效性和可靠性。进一步指出了这种通信方式具有简便性和自然的保密性的优点。

This brief addresses the channel-distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network incorporating a specific training (equalizing) algorithm.

Chaotic modulation is an important spread spectrum (SS) technique amongst chaotic communications. The logistic chaotic signal acts as the modulation signal in this paper. An adaptive demodulator based on the radial basis function (RBF) neural network is proposed. The demodulator makes use of the good approximate capacity of RBF network for a nonlinear dynamical system. Using the proposed adaptive learning algorithm, the source message can be recovered from the received SS signal. The recovering procedure is on line and adaptive. The simulated examples are included to demonstrate the new method. For the purpose of comparison, the extended-Kalman-filter-based (EKF) demodulator was also performed. The results indicate that the mean square error (MSE) of the recovered source signal by the proposed demodulator is significantly reduced, especially for the SS signal with a higher signal-to-noise ratio (SNR).

A number of schemes have been proposed for communication using chaos over the past years. Regardless of the exact modulation method used, the transmitted signal must go through a physical channel which undesirably introduces distortion to the signal and adds noise to it. The problem is particularly serious when coherent-based demodulation is used because the necessary process of chaos synchronization is difficult to implement in practice. This paper addresses the channel distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network (RNN) incorporating a specific training (equalizing) algorithm. Computer simulations are used to demonstrate the performance of the proposed equalizer in chaos-based communication systems. The H

Chaotic modulation is a useful technique for spread spectrum communication. In this paper, an on-line adaptive chaotic demodulator based on a radial-basis-function (RBF) neural network is proposed and designed. The demodulator is implemented by an on-line adaptive learning algorithm, which takes advantage of the good approximation capability of the RBF network and the tracking ability of the extended Kalman filter. It is demonstrated that, provided the modulating parameter varies slowly, spread spectrum signals contaminated by additive white Gaussian noise in a channel can be tracked in a time window, and the modulating parameter, which carries useful messages, can be estimated using the least-square fit. The Henon map is chosen as the chaos generator. Four test message signals, namely, square-wave, sine-wave, speech and image signals, are used to evaluate the performance. The results verify the ability of the demodulator in tracking the dynamics of the chaotic carrier as well as retrieving the message signal from a noisy channel.