共查询到18条相似文献,搜索用时 234 毫秒
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针对已有的混沌广义函数投影同步保密通信的局限性,研究了基于广义函数投影同步和参数调制的混沌保密通信问题。把混沌参数作为一个状态变量,将信息信号经过函数变换后调制在混沌系统的不确定参数中,通过构造参数自适应率与非线性控制器,实现混沌系统的广义函数投影同步与参数估计,在接收端通过参数辨识和反函数变换实现信息信号的解调。以电阻电容电感分流的约瑟夫森结(RCLSJJ)混沌系统为例进行数值仿真,仿真结果表明改进方法可成功辨识出系统的未知参数,从而快速有效的恢复出调制在参数中的信息信号。 相似文献
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基于小波神经网络的系统辨识方法 总被引:8,自引:2,他引:8
神经网络由于具有良好的自学习和自适应能力,在非线性黑箱建模或系统辨识中有着广泛的应用,这些辨识模型有:多层感知器、径向基函数网和反馈网络等等。文中提出了基于小波神经网络模型的系统辨识方法。由于小波变换或分解所表面的良好的时频局部化特性,以及多尺度的功能,我们用规范正交的小波函数作为基函数网络中的基函数,得到所谓的小波神经网络。通过计算机仿真证实了该方法的良好的辨识效果。 相似文献
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本文以小波理论的多分辨率分析为基础,分析了各种小波网络的频率特性,指出了由尺度函数和小波函数组成的多分辨率小波网络的优点.针对多分辨率小波网络,本文提出了一种在线辨识算法.该算法,当增加样本和新神经元时,在线修正网络权值,同时通过正交化在线优选神经元,达到优化网络结构和网络权值的目的,训练速度快,辨识精度高.仿真结果表明了该算法的有效性. 相似文献
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优化小波神经元的辨识算法 总被引:5,自引:1,他引:4
以小波理论为基础,讨论了小波径基函数网络非线性辨识的基本原理,并提出从全部小波基中选出对辨识最起作用的一部分小波基的正交优化方法,以解决小波神经网络隐层神经元数量过多的问题。用推广Kalman滤波方法训练小波网络,大大加快了它的收敛速度。仿真结果表明了该方法的可行性和有效性。 相似文献
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基于小波神经网络的通用多变量非线性系统辨识算法和应用 总被引:6,自引:0,他引:6
在分析小波函数对L2(R)空间的逼近原理的基础上,给出了仅使用尺度函数的神经网络模型和网络学习方法,使得用于逼近低通系统的小波基函数大大减少,并给出逼近的理论依据.提出的小波神经网络模型的学习为线性LS参数估计问题,具有通用性和易用性,并具有线性系统中线性LS参数估计的优良性质,保证了在训练数据受噪声污染时的网络模型的推广能力.理论分析、仿真实验和实际应用结果都说明该辨识方法具有好的辨识精度和推广能力. 相似文献
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基于正交函数逼近理论,在Haar小波正交规范基的基础上,总结并推导出了其积分运算矩阵、微分运算矩阵、乘积运算矩阵及其运算性质,并应用于一类时变非线性分布参数系统的辨识.借助于正交小波函数逼近方法对分布参数系统进行辨识,经正交小波逼近变换转化为代数矩阵方程,因此该方法可以不考虑初始条件和边界条件,较其他辨识方法要简单得多.该算法简单、计算量小、简化了分布参数系统辨识的求解过程,应用在分布参数系统辨识中不失为一种有效的分析方法. 相似文献
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Zhe Gao 《International journal of systems science》2017,48(7):1460-1471
This study proposes an identification algorithm for a time-delay fractional-order system with the measurement noise via the modulating function approach. The polynomial function is adopted as the modulating function, and the approach to determine the coefficients of the modulating function is provided. By the property of modulating function, the identified fractional-order equation is converted into an algebraic equation. By the recursive least squares estimation algorithm, the estimation method of coefficients is offered. Supposing that the measurement noise in the output signal is the Gauss white noise, a revised identification algorithm is proposed to compensate the effect of measurement noise. Finally, two examples are given to verify the effectiveness of the proposed method. 相似文献
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This study presents a nonlinear systems and function learning by using wavelet network. Wavelet networks are as neural network
for training and structural approach. But, training algorithms of wavelet networks is required a smaller number of iterations
when the compared with neural networks. Gaussian-based mother wavelet function is used as an activation function. Wavelet
networks have three main parameters; dilation, translation, and connection parameters (weights). Initial values of these parameters
are randomly selected. They are optimized during training (learning) phase. Because of random selection of all initial values,
it may not be suitable for process modeling. Because wavelet functions are rapidly vanishing functions. For this reason heuristic
procedure has been used. In this study serial-parallel identification model has been applied to system modeling. This structure
does not utilize feedback. Real system outputs have been exercised for prediction of the future system outputs. So that stability
and approximation of the network is guaranteed. Gradient methods have been applied for parameters updating with momentum term.
Quadratic cost function is used for error minimization. Three example problems have been examined in the simulation. They
are static nonlinear functions and discrete dynamic nonlinear system. 相似文献
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Identification methods have been proposed recently based on the block pulse function approach. It is shown that the same results can be obtained by using either the modulating function or the differential operator approach. Both methods provide additional insight and reveal that the choice of filtering operation with block pulse functions is somewhat arbitrary. They also serve to explain certain difficulties encountered when using block pulse functions for identification, and give directions of possible improvement 相似文献
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This paper introduces a new approach for the identification of coupled map lattice models of complex spatio-temporal patterns from measured data. The nonlinear functionals describing the evolution of the spatio-temporal patterns are constructed using B-spline wavelet and scaling functions. This provides a multiresolution approximation for the underlying spatio-temporal dynamics. An orthogonal least squares algorithm is used to determine the suitable terms from wavelet functions to form an accurate representation of the nonlinear spatio-temporal dynamics. Three examples are used to demonstrate the application of the proposed new approach. 相似文献
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In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem, an improved least squares support vector regression (LSSVR) model with multiple-kernel is proposed and the model is applied to the nonlinear separable system identification. This method utilizes the excellent nonlinear mapping ability of Morlet wavelet kernel function and combines the state and control variables information into a kernel matrix. Using the composite wavelet kernel, the LSSVR includes two nonlinear functions, whose variables are the state variables and the control ones respectively, in this way, the regression function can gain better nonlinear mapping ability, and it can simulate almost any curve in quadratic continuous integral space. Then, they are used to identify the two functions in the separable nonlinear dynamic system. Simulation results show that the multiple-kernel LSSVR method can greatly improve the identification accuracy than the single kernel method, and the Morlet wavelet kernel is more efficient than the other kernels. 相似文献