Reference Chaotic Signal Research Articles

A chaotic lock-in amplifier

We describe a method for lock-in amplification that uses a chaotic reference signal and a synchronized receiver. This technique is compared with conventional lock-in methods using a periodic reference signal. The apparatus can be constructed in an undergraduate electronics lab using a light emitting diode for the transmitter and a photodiode for the receiver, and allows students to explore a novel measurement technique based on chaotic dynamics.

A Multiple Access Scheme for Chaos-Based Digital Communication Systems Utilizing Transmitted Reference

In this paper, a multiple access (MA) technique is proposed for chaos-based communication systems, in which chaotic reference signals are transmitted followed by the information-bearing signals. Chaotic reference signals modulated by a binary training sequence are sent periodically. The same chaotic signals are then modulated by the binary data and transmitted. To achieve MA, different chaotic signals and training sequences are assigned to different users. Two types of receivers are proposed. For the first one, an adaptive filter is employed which aims to reduce the inter-user interference. For the second receiver type, a simple inverse-and-average method is used in an attempt to recover the chaotic reference signal, which is then used to correlate with the information-bearing signals for determining the received symbols. The performance bounds of these two schemes are also derived. Finally, the bit error rates of the proposed system are simulated and compared.

Smart neural control of uncertain non‐linear systems

AbstractIn this paper, we present a ‘smart’ neural control scheme for uncertain non‐linear systems using the localized radical basis function (RBF) networks. This scheme is designed such that the current control action can utilize the knowledge that the NN learned from the past control process. Compared with most existing adaptive neural controllers, which are in general very‐high‐order dynamic controllers due to the simultaneous adaptation of a large number of neural weights, the smart RBF neural controller is a static and low‐order one, and thus is more computationally feasible in practical design and implementation. To improve the generalization ability of the RBF networks, which plays an important role in the smart neural control scheme, chaotic reference signals are employed in the training phase of the scheme, where the complex chaotic signals offer richer information for NN learning due to the ergodicity of chaos. The proposed neural control scheme can act ‘smartly’ in the operational phase after the RBF networks have been well‐trained in the training phase, in a way similar to the process that humans accomplish some complicated control tasks easily after the ‘neuro‐controllers’ in their brains have been well‐trained previously. The smart neural control scheme also provides a strong motivation for the current research on chaos generation. Simulation studies are included to demonstrate the effectiveness of the new control scheme. Copyright © 2003 John Wiley & Sons, Ltd.

FUZZY ROBUST TRACKING FOR THE CHEN'S CHAOTIC ATTRACTOR

In this paper, the problem of forcing the Chen's chaotic attractor to track a sinusoidal and a chaotic reference signals in the presence of uncertainties in the system's parameter values is addressed by combining the theory of robust regulation and the exact Takagi-Sugeno fuzzy model for the Chen's chaotic attractor. On the basis of designing a robust controller for each linear subsystem, it is shown that the aggregated controller assures robust tracking in the presence of variations on the parameters of each linear subsystems and in the membership functions as well.