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基于小波网络的动态系统辨识方法及应用* 总被引:17,自引:0,他引:17
本文介绍了一种多输入非线性动态系统辨识算法,基于该算法的非线性辨识系统成功用于局部地区短时暴雨的预报。在这个系统中我们采用一种小波网络来追踪非线性系统的动态性,用一种基于小波逼近的非参数估计方法用于系统的状态空间模型的辨识中。从实验结果可看出,与传统的神经网络方法相比,该系统在速度、可靠性以及精确度上都有了很大的提高。 相似文献
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提出一种用于非线性模型在线辨识的模糊算法。该算法将非线性输入输出系统用时变线性系统模型来拟和。并把此非线性系统模型表示成模糊模型的形式,用在线调节模糊模型的方法来辨识时变线性模型的相关参数。在以往的模糊辨识方法中,均未给出在线调整非线性系统的模糊辨识算法。将递推模糊聚类方法与卡尔曼滤波法用于在线调整模糊模型参数,仿真算例表明了此算法的有效性与良好的实用价值。 相似文献
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多模型小波网络非线性动态系统辨识 总被引:1,自引:0,他引:1
由于许多复杂的工业系统具有非线性特性,难以建立确切的数学模型,因此提出用
多模型小波网络辨识非线性动态系统,并给出了辨识结构和训练算法.仿真实验比较了多模型小波网络与单小波网络在辨识非线性系统时性能上的差异,验证了该方法收敛速度快,抗干扰能力强,具有较高的逼近精度. 相似文献
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为对复杂非线性系统进行辨识建模和实施有效控制,分析了基于神经网络的非线性系统逆模型的辨识和控制原理,研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想,时常规PID控制器+前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明,基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构,可提高逆模型的泛化能力和非线性系统的控制精度。 相似文献
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该文介绍了最小模型误差估计算法(MME)和在非线性不确定系统辨识中的应用,以及分析了基于此算法的系统的鲁棒性的几个方面。并且,基于最优化理论控制以及两点边值问题,采用不变式嵌入法,给出了模型不确定非线性系统中基于MME在线递推估计算法,并首次提出了在满足协方差约束的前提下,如何最优地选择正定矩阵W的初值,以使非线性不确定系统的辨识结果达到最佳。最后,基于此算法,给出了非线性离散系统的仿真实例,仿真结果说明,最优地选择W的初值,使该方法是模型不确定非线性系统中辨识的有效方法。 相似文献
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研究了应用动态递归神经网络实现动态系统辨识的原理和方法,在没有被辨识对象的先验知识情况下,通过改进的El-man网络实现了非线性动态系统的辨识。仿真结果表明,与前馈网络相比,Elman网络具有学习速度快、泛化能力强的特点,可用较小的网络结构实现高阶系统的辨识,适用于具有本质非线性动态系统的辨识。 相似文献
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This paper proposes a new nonlinear system identification scheme using differential evolution (DE), neural network and Levenberg
Marquardt algorithm (LM). Here, DE and LM in a combined framework are used to train a neural network for achieving better
convergence of neural network weight optimization. A number of examples including a practical case-study have been considered
for implementation of different system identification methods namely, only NN, DE+NN and DE+LM+NN. After, a series of simulation
studies of these methods on the different nonlinear systems it has been confirmed that the proposed DE and LM trained NN approach
to nonlinear system identification has yielded better identification results in terms of time of convergence and less identification
error. 相似文献
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This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input–multi-output system (TRMS) to verify the identification performance. 相似文献
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非线性动态系统的Wiener神经网络辨识法 总被引:2,自引:0,他引:2
提出了一种新的Wiener神经网络结构并将其应用于非线性动态系统辨识问题.首先,用Wiener模型对非线性动态系统进行描述,将其分解成线性动态子环节串接非线性静态增益的形式.其次,设计一种新型的神经网络结构,使网络权值对应于相应的Wiener模型参数;并推导了基于反向传播的网络权值调整方法.最后,通过网络迭代训练,可同时得到线性动态子环节和非线性静态增益的模型参数.通过一个Wiener模型的数值仿真来验证方法的有效性,仿真结果表明所提辨识方法切实可行. 相似文献
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On the persistency of excitation in radial basis function networkidentification of nonlinear systems 总被引:1,自引:0,他引:1
Considers radial basis function (RBF) network approximation of a multivariate nonlinear mapping as a linear parametric regression problem. Linear recursive identification algorithms applied to this problem are known to converge, provided the regressor vector sequence has the persistency of excitation (PE) property. The main contribution of this paper is formulation and proof of PE conditions on the input variables. In the RBF network identification, the regressor vector is a nonlinear function of these input variables. According to the formulated condition, the inputs provide PE, if they belong to domains around the network node centers. For a two-input network with Gaussian RBF that have typical width and are centered on a regular mesh, these domains cover about 25% of the input domain volume. The authors further generalize the proposed solution of the standard RBF network identification problem and study affine RBF network identification that is important for affine nonlinear system control. For the affine RBF network, the author formulates and proves a PE condition on both the system state parameters and control inputs. 相似文献
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讨论了利用小波神经网络对非线性系统辨识的新方法。在辨识过程中,为了提高小波神经网络对非线性系统的辨识性能,使用一种改进粒子群优化算法对BP小波神经网络参数进行训练,求得最优值,达到对非线性系统辨识目的。在数值仿真中,与采用标准粒子群优化算法相比,结果显示了提出的方法在收敛性和稳定性等方面均得到了明显的改善。 相似文献
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Xuan HanAuthor VitaeWen-Fang XieAuthor Vitae Zhijun FuAuthor VitaeWeidong LuoAuthor Vitae 《Neurocomputing》2011,74(17):3428-3439
In this paper, two Neural Network (NN) identifiers are proposed for nonlinear systems identification via dynamic neural networks with different time scales including both fast and slow phenomena. The first NN identifier uses the output signals from the actual system for the system identification. The on-line update laws for dynamic neural networks have been developed using the Lyapunov function and singularly perturbed techniques. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the neuron networks. The on-line identification algorithm with dead-zone function is proposed to improve nonlinear system identification performance. Compared with other dynamic neural network identification methods, the proposed identification methods exhibit improved identification performance. Three examples are given to demonstrate the effectiveness of the theoretical results. 相似文献
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人工神经元网络在系统辨识中的应用 总被引:7,自引:1,他引:6
本文将人工神经元网络的非线性性和信息的分布性用于非线性静态模型的辨识.对化
工生产中的缩聚反应过程的辨识结果表明,用人工神经元网络来辨识非线性静态模型是可行
的,从而为系统辨识提供了一条新的途径. 相似文献