Jianye Zhao

Using Chaotic Neural Network to Forecast Stock Index

In this paper, a new scheme based on chaotic neural network for stock index prediction is proposed. The data from a Chinese stock market, Shenzhen stock market, are applied as a case study. The chaotic neural network is used to learn the non-linear stochastic and chaotic patterns in the stock system and forecast a new index with former indexes. The validity of the scheme is analyzed theoretically, and the simulation results show that it has a good performance.

A novel Chaotic Stream DS-UWB system

The novel chaotic stream DS-UWB system proposed in this paper accomplishes synchronization, modulation and encryption of data in only one channel transmission mechanism. The architecture of the system combines the chaotic pulse position modulation, the complex chaotic stream ciphers encryption and the chaotic direct spread codes with the PAM based DS-UWB communication system. The synchronization of the system is robust with noise and distortion. To overcome problems caused by the digital finite precision, the cipher systems are designed carefully to guarantee Shannonpsilas three principles of secure systems. The chaotic direct spread code is changing with time just as the chaotic stream ciphers, which provides a better performance on security, spectrum and multi-access. Results of simulations show that the chaotic stream DS-UWB has much better BER performances (3~5 dB) than the ordinary DS-UWB when the channel is multi-path channel.

Information transfer characteristic in memristic neuromorphic network

Memristive nanodevices can support exactly the same learning function as spike-timing-dependent plasticity in neuroscience, and thus the exploration for the evolution and self-organized computing of memristor-based neuromorphic networks becomes reality. We mainly study the STDP-driven refinement effect on memristor-based crossbar structure and its information transfer characteristic. The results show that self-organized refinement could enhance the information transfer of memristor crossbar, and the dependence of memristive device on current direction and the balance between potentiation and depression are of crucial importance. This gives an inspiration for resolving the power consumption issue and the so called sneak path problem.

An Adaptive Radar Target Signal Processing Scheme Based on AMTI Filter and Chaotic Neural Networks

In the proposed new scheme of adaptive radar target signal processing, the chaotic neural network not only detects the target signal by reconstructing the chaotic clutter, but also repairs the frequency spectrum according to its associative memory characteristic. The clutter is filtered by the Burg algorithm based on the adaptive MTI filter. The information of distance and velocity is also obtained by Burg spectral estimation. The validity of the scheme is analyzed theoretically, and the simulation results show that it has good performance in clutter and noise background. The adaptive method adopted in this paper facilitates the radar design in complex environment.

A New Approach for Image Restoration Based on CNN Processor

A new approach for maximum posterior probability (MAP) image restoration based on cellular neural network (CNN) is proposed in this paper, and hardware realization is also discussed. According to analysis of MAP image restoration, a new template is proposed for CNN image restoration. The computer simulation result proves the approach is reasonable, then a hardware system based on CNN processor is setup for the restoration algorithm, and the effectiveness of the CNN processor is also confirmed in this system.

Detecting Moving Targets in Ground Clutter Using RBF Neural Network

In this paper, a new structure for moving targets detection and characteristics extraction in ground clutter is proposed. This structure combines Radial Basis Function (RBF) neural network, Burg algorithm, and notch filter. After dynamical reconstruction, the RBF network is used to predict the ground clutter. Spectral characteristics of the ground clutter are estimated using the Burg algorithm. We apply notch filter to cancel the interference caused by the ground clutter. Moreover, a hardware platform based on FPGA is also realized for this paper to demonstrate this proposed structure and sufficient details of the hardware platform are discussed. The results of simulation and hardware implementation show that the presented structure has a good performance in processing target signals mixed with the ground clutter.

Experimental Spread Spectrum Communication System Based on CNN

A new spread spectrum communication system based on CNN is proposed in this paper. Chaos is generated with three cell CNN, then it’s transferred to a digital sequence. The chaotic sequence is better than gold sequence when they are utilized in direct sequence spread spectrum system. Compared with the traditional gold sequence system, there is 2dB improvement in CNN chaotic sequence system when the channel is additive white Gaussian noise channel, and there is more improvement in CNN chaotic sequence system when the channel is multi-path channel. The structure of hardware CNN spread spectrum system is also shown at last.