首页> 外文期刊>Neural Computing and Applications >Hyperchaos synchronization using PSO-optimized RBF-based controllers to improve security of communication systems
【24h】

Hyperchaos synchronization using PSO-optimized RBF-based controllers to improve security of communication systems

机译:使用基于PSO优化的基于RBF的控制器进行超混沌同步,以提高通信系统的安全性

获取原文
获取原文并翻译 | 示例

摘要

This paper studies the Lorenz hyperchaos synchronization and its application to improve the security of communication systems. Two methods are proposed to synchronize the general forms of hyperchaotic systems, and their performance in secure communication application is verified. These methods use the radial basis function (RBF)-based neural controllers for this purpose. The first method uses a standard RBF neural controller. Particle swarm optimization (PSO) algorithm is used to derive and optimize the parameters of the RBF controller. In the second method, with the aim of increasing the robustness of the RBF controller, an error integral term is added to the equations of RBF neural network. For this method, the coefficients of the error integral component and the parameters of RBF neural network are also derived and optimized via PSO algorithm. For better comparison, the proposed methods and an optimal PID controller optimized by PSO are applied to the Lorenz hyperchaotic system for secure communication. Simulation results show the effectiveness and superiority of the proposed methods in both performance and robustness in comparison with the PID controller.
机译:本文研究了Lorenz超混沌同步及其在提高通信系统安全性中的应用。提出了两种方法来同步超混沌系统的一般形式,并验证了它们在安全通信应用中的性能。这些方法为此使用基于径向基函数(RBF)的神经控制器。第一种方法使用标准的RBF神经控制器。粒子群优化(PSO)算法用于导出和优化RBF控制器的参数。在第二种方法中,为了提高RBF控制器的鲁棒性,将误差积分项添加到RBF神经网络的方程式中。对于该方法,还通过PSO算法推导并优化了误差积分分量的系数和RBF神经网络的参数。为了更好地进行比较,将所提出的方法和通过PSO优化的最优PID控制器应用于Lorenz超混沌系统进行安全通信。仿真结果表明,与PID控制器相比,所提方法在性能和鲁棒性方面均有效且优越。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号