New Controller Design in QCNN and the Synchronization with Chaotic System

2015 ◽  
Vol 719-720 ◽  
pp. 359-364
Author(s):  
Chang Xing Chen ◽  
De Zhi Niu ◽  
Hui Fu ◽  
Yan Ming Zhao ◽  
Xu Jing Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Controller Design ◽  
Reference Signal ◽  
Periodic Signal ◽  
Weak Signal ◽  
New Thought ◽  
Duffing System ◽  
Synchronization Problem ◽  
Simulation Results ◽  
Synchronization Structure

Synchronization problem between QCNN (Quantum Cellular Neural Networks) and duffing system was studied in the paper. Not only one new controller with less parameters in QCNN was designed, but it was proved by Lyapunov function that output signal would be convergent to reference signal, and meanwhile, forms of multi-dimension controllers were given. Then the synchronization structure was draw with the corresponding controllers. In the simulation results, the new controller has higher time efficiency, which shows applying the new controller to QCNN is feasible and effective and it can assure the achievement of synchronization. Furthermore, weak periodic signal can be detected in QCNN, which provides one new thought to detection of weak signal.

Parameter Estimation of Co-Channel AIS Signal Using Ambiguity Function

2014 ◽  
Vol 644-650 ◽  
pp. 4035-4039
Author(s):  
Hao Su Zhou ◽  
Jian Xin Wang
Keyword(s):  
Parameter Estimation ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Reference Signal ◽  
Two Dimensions ◽  
Ambiguity Function ◽  
Weak Signal ◽  
Strong Signal ◽  
Estimated Parameters ◽  
Simulation Results

A new data-aided algorithm for parameter estimation of the co-channel AIS signal transmitted over the additive white Gaussian noise channel is proposed in this paper. The co-channel signal consists of a strong signal with high power and a weak signal with low power. The parameters of the strong signal are estimated by searching the ambiguity function of the co-channel signal in two dimensions. A reference signal is therefore reconstructed with the estimated parameters and the aided data. By removing the ambiguity function of the reconstructed reference signal from that of the original co-channel signal, a new co-channel signal ambiguity function is obtained, from which the parameters of the weak signal are estimated. The simulation results illustrate that the proposed algorithm can estimate the parameters of the co-channel AIS signal effectively.

Variance Based Identification of Phase Transition in Duffing Oscillator for Weak Signal Detection

2011 ◽  
Vol 128-129 ◽  
pp. 354-358 ◽  
Author(s):  
Yuan Chang ◽  
Chun Wen Li ◽  
Yi Hao
Keyword(s):  
Phase Transition ◽  
Signal Detection ◽  
Periodic Motion ◽  
Sharp Increase ◽  
Duffing Oscillator ◽  
Periodic Signal ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Periodic Signals ◽  
Simulation Results

This paper studies the detection of weak signal detection using a Duffing Oscillator, which is sensitive to periodic signals but insensitive to noises. The system transits from chaotic to great periodic motion when coupled to the weak periodic signal to be detected. To efficiently determine the phase transition, a novel numerical criterion is proposed based on the sharp increase of variance when phase change happens. Simulation results verified the effectiveness of this method.

scholarly journals A Method of Changing the Center Position in Duffing Chaotic Systsem

2018 ◽  
Vol 228 ◽  
pp. 01007
Author(s):  
Jianqun Han ◽  
Xudong Shi
Keyword(s):  
Theoretical Analysis ◽  
Computer Simulations ◽  
Secure Communication ◽  
Simulation System ◽  
Periodic Signal ◽  
Chaotic State ◽  
Duffing System ◽  
Mathematical Proofs ◽  
Center Position ◽  
Simulation Results

Under the condition of keeping Duffing system in chaotic state, we present a method of changing the center position by adding a non-smooth periodic signal to Duffing system. In this paper, we first make a theoretical analysis which describe the influence of added signal on center point of Duffing system and provide mathematical proofs, and then build a simulation system which can change the center position of Duffing system. Finally, computer simulations are used to verify the analytical results. Simulation results show that the proposed method can effectively work in secure communication applications.

Study on Heterodyne Stochastic Resonance System for Weak Signal Detection

2011 ◽  
Vol 88-89 ◽  
pp. 341-345
Author(s):  
Jun Jun Yao ◽  
Yong Mei Huang ◽  
Li Qing Hu ◽  
Min Lin
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Frequency Measurement ◽  
Periodic Signal ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Resonance System ◽  
Resonance Theory ◽  
System Parameters ◽  
Simulation Results

The method based on stochastic resonance is a newly developed signal processing technology. Different from other methods by restraining the noise, it takes full advantage of the noises to strengthen the weak signal to improve SNR of the system. Based on stochastic resonance theory, a heterodyne system is presented in the paper to detect the frequency of the weak periodic signal. The simulation results manifest that the method is effective and the system parameters have extensive influence for frequency measurement. The parameters of the system are investigated for optimizing system design and weak signal detection.

Weak Signal Detection Principle Based on Chaotic Duffing Oscillator and its Simulation Method

2010 ◽  
Vol 108-111 ◽  
pp. 834-837
Author(s):  
Ming Shan Cai
Keyword(s):  
Signal Detection ◽  
Virtual Instrument ◽  
Duffing Oscillator ◽  
Sampling Period ◽  
Periodic Signal ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Weak Signals ◽  
Simulation Results ◽  
Detection Principle

Based on Matlab as the software platform, chaos detection principle and methods used for weak signals detection are studied. The model used for simulation is established, then the simulation results of weak periodic signal detection in strong noise atmosphere are given and the steps for detecting weak signals with chaos method are listed. Furthermore, the influence of sampling period on system’s performance is studied. Simulation results show that the chao detection approach proposed in this paper can detect the signal even if it is small to 10-10v, and even when it is in the environment with strong noise, small signal with magnitude of only 5×10-9 v can be found. Chaos method has strong capability for weak signal detection which lay important foundation for exploiting virtual instrument.

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

IMPROVED NEURAL NETWORKS FOR LINEAR AND NONLINEAR PROGRAMMING

1991 ◽  
Vol 02 (04) ◽  
pp. 331-339 ◽  
Author(s):  
Jiahan Chen ◽  
Michael A. Shanblatt ◽  
Chia-Yiu Maa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Programming ◽  
Penalty Function ◽  
New Combination ◽  
Optimal Point ◽  
Linear And Nonlinear Programming ◽  
Linear And Nonlinear ◽  
Simulation Results

A method for improving the performance of artificial neural networks for linear and nonlinear programming is presented. By analyzing the behavior of the conventional penalty function, the reason for the inherent degenerating accuracy is discovered. Based on this, a new combination penalty function is proposed which can ensure that the equilibrium point is acceptably close to the optimal point. A known neural network model has been modified by using the new penalty function and the corresponding circuit scheme is given. Simulation results show that the relative error for linear and nonlinear programming is substantially reduced by the new method.

scholarly journals Synchronization of Switched Interval Networks and Applications to Chaotic Neural Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jinde Cao ◽  
Abdulaziz Alofi ◽  
Abdullah Al-Mazrooei ◽  
Ahmed Elaiw
Keyword(s):  
Neural Networks ◽  
Matrix Measure ◽  
Chaotic Neural Networks ◽  
Time Varying Delay ◽  
Synchronization Problem ◽  
Arbitrary Switching ◽  
Parameters Uncertainty ◽  
Inequality Technique ◽  
Error System ◽  
Switched Interval Networks

This paper investigates synchronization problem of switched delay networks with interval parameters uncertainty, based on the theories of the switched systems and drive-response technique, a mathematical model of the switched interval drive-response error system is established. Without constructing Lyapunov-Krasovskii functions, introducing matrix measure method for the first time to switched time-varying delay networks, combining Halanay inequality technique, synchronization criteria are derived for switched interval networks under the arbitrary switching rule, which are easy to verify in practice. Moreover, as an application, the proposed scheme is then applied to chaotic neural networks. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

Virtual Instrument for Weak Signal Detecting on Monostable Stochastic Resonance

2011 ◽  
Vol 279 ◽  
pp. 361-366
Author(s):  
Quan Yuan ◽  
Yan Shen ◽  
Liang Chen
Keyword(s):  
Power Spectrum ◽  
White Noise ◽  
Stochastic Resonance ◽  
Virtual Instrument ◽  
Stable System ◽  
Periodic Signal ◽  
Nonlinear Phenomenon ◽  
Weak Signal ◽  
Additive White Noise ◽  
Periodic Signals

Stochastic resonance (SR) is a nonlinear phenomenon which can be used to detect weak signal. The theory of SR in a biased mono-stable system driven by multiplicative and additive white noise as well as a weak periodic signal is investigated. The virtual instrument (VI) for weak signal detecting based on this theory is designed with LabVIEW. This instrument can be used to detect weak periodic signals which meets the conditions given and can greatly improved the power spectrum of the weak signal. The results that related to different sets of parameters are given and the features of these results are in accordance with the theory of mono-stable SR. Thus, the application of this theory in the detecting of weak signal is proven to be valid.

Digital Controller Design for Half-Car Active Suspension System with Using Singular Perturbation Theory

2011 ◽  
Vol 403-408 ◽  
pp. 4800-4805 ◽  
Author(s):  
A. R. Paarya ◽  
H. Zarabadipour
Keyword(s):  
Perturbation Theory ◽  
Controller Design ◽  
Suspension System ◽  
Digital Controller ◽  
Singular Perturbation Theory ◽  
Vehicle Suspension ◽  
Car Model ◽  
Perturbed Systems ◽  
Simulation Results ◽  
Vehicle Suspension System

In this paper the digital controller design for vehicle suspension system, based on a half-car model using singular perturbed systems is considered. This strategy is based on the slow and fast subsystems controller design. The simulation results show them favorable performance of the controller and achieve fast and good response.

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