共查询到20条相似文献,搜索用时 156 毫秒
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采用遗传编程方法解决非单调非线性系统的辨识问题。在对象的结构和参数未知的情况下,先进行参数恒定下的辨识.再进行在运行期间非线性环节参数发生变化的系统辨识。试验结果表明:都能比较迅速地得出非线性部分直观的、近似的数学表达式。提出了今后进一步完善的几点设想。 相似文献
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基于连锁聚类法及遗传算法的模糊建模 总被引:1,自引:0,他引:1
模糊建模可以分为被辨识系统的结构辨识和参数辨识.针对系统的结构辨识,提出了一种新型连锁聚类算法,用其来实现被辨识系统的结构辨识及初始参数辨识;针对系统的参数辨识,提出了采用遗传算法对被辨识系统的参数进行更加精确的校正.通过结构辨识算法和参数辨识算法的结合,可以只针对被辨识系统的输入输出测试数据直接进行被辨识系统的结构辨识及参数的进一步精确校正.通过对非线形函数的仿真结果表明,此辨识方法具有较好的辨识结果. 相似文献
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研究非线性系统辨识问题.针对非线性系统中单输入单输出Hammerstein模型,由于传统辨识方法对Hammerstein模型中非线性部分具有不易辨识的缺陷,造成辨识精度低、辨识效果差等问题.为此,在基本粒子群算法的基础上,提出了一种带有收缩因子的改进的粒子群算法对非线性系统进行辨识的方法,可将参数辨识问题转换为参数空间上的函数优化问题,然后利用粒子群算法的并行搜索能力进行参数寻优.通过MATLAB软件进行仿真,并与基本粒子群算法进行比较,结果表明,利用改进算法不仅提高了辨识精度而且获得了良好的辨识效果,从而验证了算法的有效性和可行性. 相似文献
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关于非线性自动控制系统优化问题,为解决复杂非线性系统的辨识问题,提出了一种基于菌群优化算法的非线性系统辨识方法.结合菌群优化算法的特点,通过将待辨识参数设置为群体细菌在参数空间的位置,并利用细菌群体觅食的动态行为来实现对系统参数的辨识,有效地提高了参数辨识的精度和效率.通过对重油热解三集总模型进行了仿真研究,得到了较为精确的过程模型,模型输出与实际输出基本一致.仿真结果表明:菌群优化算法为非线性系统模型参数估计提供了一种有效的途径. 相似文献
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A novel use of neural networks for parameter estimation in nonlinear systems is proposed. The approximating ability of the neural network is used to identify the relation between system variables and parameters of a dynamic system. Two different algorithms, a block estimation method and a recursive estimation method, are proposed. The block estimation method consists of the training of a neural network to approximate the mapping between the system response and the system parameters which in turn is used to identify the parameters of the nonlinear system. In the second method, the neural network is used to determine a recursive algorithm to update the parameter estimate. Both methods are useful for parameter estimation in systems where either the structure of the nonlinearities present are unknown or when the parameters occur nonlinearly. Analytical conditions under which successful estimation can be carried but and several illustrative examples verifying the behavior of the algorithms through simulations are presented. 相似文献
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This paper proposes an identification method for Hammerstein systems using simultaneous perturbation stochastic approximation (SPSA). Here, the structure of nonlinear subsystem is assumed to be unknown, while the structure of linear subsystem, such as the system order, is assumed to be available. The main advantage of the SPSA-based method is that it can be applied to identification of Hammerstein systems with less restrictive assumptions. In order to clarify this point, piecewise affine functions with a large number of parameters are adopted to approximate the unknown nonlinear subsystems. Furthermore, the linear subsystems are supposed to be described in continuous-time. Though this class of systems closely reflects the actual systems, there are few methods to identify such models. Hence, the SPSA-based method is utilized to identify the parameters in both linear and nonlinear subsystems simultaneously. The effectiveness of the proposed method is evaluated through several numerical examples. The results demonstrate that the proposed algorithm is useful to obtain accurate models, even for high-dimensional parameter identification. 相似文献
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Modelling and control of nonlinear,operating point dependent systems via associative memory networks
This paper presents a novel approach to the modelling and control of a specific class of nonlinear systems whose parameters are unknown nonlinear functions of the measurable operating points. An associative memory network is used to identify each nonlinear function, whose inputs are the measurable operating points and output being the estimated value of the parameter. Two different cases are considered; the first being those systems where the networks can exactly model the nonlinear functions, whereas the second case considers those systems which can only approximate the nonlinear functions toa known accuracy. The first type of system is referred to as a matching system and the second is called a mismatching system. During the modelling phase, the weights for each network are trained in parallel using the normalised back-propagation algorithm for matching system, and the modified recursive least squares algorithm for mismatching systems. It has been shown that these algorithms together withGoodwin's technical lemma lead to a stable d-step-ahead control scheme for matching systems and a pole assignment control strategy for mismatching systems. 相似文献
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高阶常微分方程的演化建模用于时间序列的分析 总被引:2,自引:1,他引:1
本文提出采用高阶常微分方程模型代替传统的时序分析中所用的ARMA模型来实现一维动态系统的建模,并针对传统方法建模过程中所遇到的困难,设计了将遗传程序设计与遗传算法个嵌套的混合演化建模算法,以遗传程序设计优化模型结构,以遗传算法优化模型参数,首次成功地实现了动态系统的高阶微分方程建模过程自动化,对三个典型时间序列实例的实验结果表明:采用此算法可由计算机自动发现适合描述该动态系统的高阶常微分方程模型, 相似文献
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Nonlinear systems subjected to Gaussian inputs are studied based on the Wiener-like stochastic functional Fourier series [1]. Analytic and nonanalytic cascade, bilinear, and feedback nonlinear structures are considered and theirn th-order Fourier-Hermite kernels are calculated analytically. The characteristic kernel features thus revealed are discussed as a guide to interpret data from multidimensional cross-correlation experiments for nonparametric nonlinear system identification. The results are shown to be useful also for the mean-square-signal analysis of nonlinear systems whose structures and parameters are known a priori. For the feedback case, a certain approximation is employed for finding then th-order closed-loop kernel. This is a generalization of the describing function technique, and using examples, the algorithm is compared to existing procedures for random-input nonlinear servosynthesis. 相似文献
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