Signal separation in an aggregation of chaotic signals

Zandi-Mehran, Nazanin; Jafari, Sajad; Golpayegani, Seyed Mohammad Reza Hashemi

doi:10.1016/j.chaos.2020.109851

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Compared with random systems, chaotic systems are more sensitive to initial values and have the characteristics of mixing properties, randomness, ergodicity and regularity [ 28 , 29 , 30 ], which can produce uniform and stable orbits. Given a long enough time, a chaotic system can traverse all states of the space, and the traversal process never repeats in a certain range, so it can effectively circumvent the local optimum.…”

Section: Improved Algorithmmentioning

confidence: 99%

Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm

Zhang

et al. 2023

Sensors

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The Aquila Optimizer (AO) is a new bio-inspired meta-heuristic algorithm inspired by Aquila’s hunting behavior. Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm (NCAAO) is proposed to address the problem that although the Aquila Optimizer (AO) has a strong global exploration capability, it has an insufficient local exploitation capability and a slow convergence rate. First, to improve the diversity of populations in the algorithm and the uniformity of distribution in the search space, DLCS chaotic mapping is used to generate the initial populations so that the algorithm is in a better exploration state. Then, to improve the search accuracy of the algorithm, an adaptive adjustment strategy of de-searching preferences is proposed. The exploration and development phases of the NCAAO algorithm are effectively balanced by changing the search threshold and introducing the position weight parameter to adaptively adjust the search process. Finally, the idea of small habitats is effectively used to promote the exchange of information between groups and accelerate the rapid convergence of groups to the optimal solution. To verify the optimization performance of the NCAAO algorithm, the improved algorithm was tested on 15 standard benchmark functions, the Wilcoxon rank sum test, and engineering optimization problems to test the optimization-seeking ability of the improved algorithm. The experimental results show that the NCAAO algorithm has better search performance and faster convergence speed compared with other intelligent algorithms.

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Section: Improved Algorithmmentioning

confidence: 99%

Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm

Zhang

et al. 2023

Sensors

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Dynamical behavior of the optical traveling pulses for the resonant nonlinear Schrödinger equation with external periodic force

Das

Saha

Ghosh

2020

Int. J. Mod. Phys. B

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Dynamical behavior of the optical traveling pulses for the resonant nonlinear Schrödinger (RNS) equation with external periodic force is studied. Using a complex transformation we obtain an unperturbed dynamical system for the RNS equation. Existence of periodic optical pulses, solitary optical pulses of dark and bright types, breaking optical pulses is dispensed using phase plane analysis of the unperturbed dynamical system. Introducing an external perturbation to the unperturbed dynamical system, quasiperiodicity and chaotic features of the nonlinear optical pulses for the perturbed dynamical system are studied by varying the resonance parameter (c) with special values of other system parameters through different computational tools, like time series plot, phase plot, sensitivity plot, Lyapunov exponent, and Poincare section. The resonance parameter (c) acts as a control parameter on qualitative transition of the nonlinear optical pulses for the perturbed dynamical system from quasiperiodic motion to chaotic motion.

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Accurate separation of mixed high-dimension optical-chaotic signals using optical reservoir computing based on optically pumped VCSELs

Zhong¹,

Hu²,

Zhao³

et al. 2022

Opt. Express

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In this work, with the mixing fractions being known in advance or unknown, the schemes and theories for the separations of two groups of the mixed optical chaotic signals are proposed in detail, using the VCSEL-based reservoir computing (RC) systems. Here, two groups of the mixed optical chaotic signals are linearly combined with many beams of the chaotic x-polarization components (X-PCs) and Y-PCs emitted by the optically pumped spin-VCSELs operation alone. Two parallel reservoirs are performed by using the chaotic X-PC and Y-PC output by the optically pumped spin-VCSEL with both optical feedback and optical injection. Moreover, we further demonstrate the separation performances of the mixed chaotic signal linearly combined with no more than three beams of the chaotic X-PC or Y-PC. We find that two groups of the mixed optical chaos signals can be effectively separated by using two reservoirs in single RC system based on optically pumped Spin-VCSEL and their corresponding separated errors characterized by the training errors are no more than 0.093, when the mixing fractions are known as a certain value in advance. If the mixing fractions are unknown, we utilize two cascaded RC systems based on optically pumped Spin-VCSELs to separate each group of the mixed optical signals. The mixing fractions can be accurate predicted by using two parallel reservoirs in the first RC system. Based on the values of the predictive mixing fractions, two groups of the mixed optical chaos signals can be effectively separated by utilizing two parallel reservoirs in the second RC system, and their separated errors also are no more than 0.093. In the same way, the mixed optical chaos signal linearly superimposed with more than three beams of optical chaotic signals can be effectively separated. The method and idea for separation of complex optical chaos signals proposed by this paper may provide an impact to development of novel principles of multiple access and demultiplexing in multi-channel chaotic cryptography communication.

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Signal separation in an aggregation of chaotic signals

Cited by 3 publications

References 21 publications

Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm

Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm

Dynamical behavior of the optical traveling pulses for the resonant nonlinear Schrödinger equation with external periodic force

Accurate separation of mixed high-dimension optical-chaotic signals using optical reservoir computing based on optically pumped VCSELs

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