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1.
确定学习与基于数据的建模及控制   总被引:5,自引:1,他引:4       下载免费PDF全文
确定学习运用自适应控制和动力学系统的概念与方法, 研究未知动态环境下的知识获取、表达、存储和利用等问题. 针对产生周期或回归轨迹的连续 非线性动态系统, 确定学习可以对其未知系统动态进行局部准确建模, 其基本要 素包括: 1)使用径向基函数(Radial basis function, RBF)神经网络; 2)对于周期(或回归)状态轨迹 满足部分持续激励条件; 3)在周期(或回归)轨迹的邻域内实现对非线性系统动态的局部准确神经网络逼近(局部准确建模); 4)所学的知识以时不变且空间分布的方式表达、以常值神经网络权值的方式存储, 并可在动态环境下用于动态模式的快速识别或者闭环神经网络控制. 本文针对离散动态系统, 扩展了确定学习理论, 提出一个根据时态数据序列对离散动态系统进行建模与控制的框架. 首先, 运用确定学习原理和离散系统的自适应辨识方法, 实现对产生时态数据的离散非线性系统的未知动态进行局部准确的神经网络建模, 并利用此建模结果对时态数据序列进行时不变表达. 其次, 提出时态数据序列的基于动力学的相似性定义, 以及对离散动态系统产生的时态数据序列(亦可称为动态模式)进行快速识别方法. 最后, 针对离散非线性控制系统, 实现了基于时态数据序列对控制系统动态的闭环辨识(局部准确建模). 所学关于闭环动态的知识可用于基于模式的智能控制. 本文表明确定学习可以为时态数据挖掘的研究提供新的途径, 并为基于数据的建模与控制等问题提供新的研究思路.  相似文献
2.
Learning from neural control of nonlinear systems in normal form   总被引:4,自引:0,他引:4  
A deterministic learning theory was recently proposed which states that an appropriately designed adaptive neural controller can learn the system internal dynamics while attempting to control a class of simple nonlinear systems. In this paper, we investigate deterministic learning from adaptive neural control (ANC) of a class of nonlinear systems in normal form with unknown affine terms. The existence of the unknown affine terms makes it difficult to achieve learning by using previous methods. To overcome the difficulties, firstly, an extension of a recent result is presented on stability analysis of linear time-varying (LTV) systems. Then, with a state transformation, the closed-loop control system is transformed into a LTV form for which exponential stability can be guaranteed when a partial persistent excitation (PE) condition is satisfied. Accurate approximation of the closed-loop control system dynamics is achieved in a local region along a recurrent orbit of closed-loop signals. Consequently, learning of control system dynamics (i.e. closed-loop identification) from adaptive neural control of nonlinear systems with unknown affine terms is implemented.  相似文献
3.
吴玉香  王聪 《自动化学报》2013,39(6):806-815
针对产生回归轨迹的连续非线性动态系统, 确定学习可实现未知闭环系统动态的局部准确逼近. 基于确定学习理论, 本文使用径向基函数(Radial basis function, RBF)神经网络为机器人任务空间跟踪控制设计了一种新的自适应神经网络控制算法, 不仅实现了闭环系统所有信号的最终一致有界, 而且在稳定的控制过程中, 沿着回归跟踪轨迹实现了部分神经网络权值收敛到最优值以及未知闭环系统动态的局部准确逼近. 学过的知识以时不变且空间分布的方式表达、以常值神经网络权值的方式存储, 可以用来改进系统的控制性能, 也可以应用到后续相同或相似的控制任务中, 节约时间和能量. 最后, 用仿真说明了所设计控制算法的正确性和有效性.  相似文献
4.
Performance of deterministic learning in noisy environments   总被引:1,自引:0,他引:1  
In this paper, based on the previous results of deterministic learning, we investigate the performance of deterministic learning in noisy environments. Two different types of noises arising in practical implementations are considered: the system noise and the measurement noise. By employing the convergence results of a class of perturbed linear time-varying (LTV) systems, the effects of these noises upon the learning performance are revealed. It is shown that while there is little effect upon the learning speed, noises have much influence on the learning accuracy. Compared with system noise, the effects of measurement noise appear to be more complicated. Under the noisy environments, robustification technique on the learning algorithm is required to avoid parameter drift. Furthermore, it is shown that additive system noise can be used to enhance the generalization ability of the RBF networks. Simulation studies are included to illustrate the results.  相似文献
5.
Persistency of excitation and performance of deterministic learning   总被引:1,自引:0,他引:1  
Recently, a deterministic learning theory was proposed for locally-accurate identification of nonlinear systems. In this paper, we investigate the performance of deterministic learning, including the learning speed and learning accuracy. By analyzing the convergence properties of a class of linear time-varying (LTV) systems, explicit relations between the persistency of excitation (PE) condition (especially the level of excitation) and the convergence properties of the LTV systems are derived. It is shown that the learning speed increases with the level of excitation and decreases with the upper bound of PE. An optimal learning speed is shown to exist. The learning accuracy also increases with the level of excitation, in particular, when the level of excitation is large enough, locally-accurate learning can be achieved to the desired accuracy, whereas low level of PE may result in the deterioration of the learning performance. This paper reveals that the performance analysis of deterministic learning can be established on the basis of classical results on stability and convergence of adaptive control. Simulation studies are included to illustrate the results.  相似文献
6.
We derive cost formulae for three different parallelisation techniques for training both supervised and unsupervised networks. These formulae are parameterised by properties of the target computer architecture. It is therefore possible to decide both which technique is best for a given parallel computer, and which parallel computer best suits a given technique. One technique, exemplar parallelism, is far superior to almost all parallel computer architectures. Formulae also take into account optimal batch learning as the overall training approach. Cost predictions are made for several of today's popular parallel computers.  相似文献
7.
In this paper, we present a new silhouette-based gait recognition method via deterministic learning theory, which combines spatio-temporal motion characteristics and physical parameters of a human subject by analyzing shape parameters of the subject?s silhouette contour. It has been validated only in sequences with lateral view, recorded in laboratory conditions. The ratio of the silhouette?s height and width (H–W ratio), the width of the outer contour of the binarized silhouette, the silhouette area and the vertical coordinate of centroid of the outer contour are combined as gait features for recognition. They represent the dynamics of gait motion and can more effectively reflect the tiny variance between different gait patterns. The gait recognition approach consists of two phases: a training phase and a test phase. In the training phase, the gait dynamics underlying different individuals? gaits are locally accurately approximated by radial basis function (RBF) networks via deterministic learning theory. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the test phase, a bank of dynamical estimators is constructed for all the training gait patterns. The constant RBF networks obtained from the training phase are embedded in the estimators. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated, and the average L1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, the recognition performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches on the most well-known public gait databases: CASIA, CMU MoBo and TUM GAID.  相似文献
8.
In this paper, we extend the deterministic learning theory to sampled-data nonlinear systems. Based on the Euler approximate model, the adaptive neural network identifier with a normalized learning algorithm is proposed. It is proven that by properly setting the sampling period, the overall system can be guaranteed to be stable and partial neural network weights can exponentially converge to their optimal values under the satisfaction of the partial persistent excitation (PE) condition. Consequently, locally accurate learning of the nonlinear dynamics can be achieved, and the knowledge can be represented by using constant-weight neural networks. Furthermore, we present a performance analysis for the learning algorithm by developing explicit bounds on the learning rate and accuracy. Several factors that influence learning, including the PE level, the learning gain, and the sampling period, are investigated. Simulation studies are included to demonstrate the effectiveness of the approach.  相似文献
9.
考虑到实现确定学习理论中的动态模式识别过程耗时过多,提出一种适用于识别过程计算的多核并行技术。以压气机Mansoux模型为研究背景,首先对其模式获取和识别的过程进行简单描述,其次,在四核PC的硬件平台上,使用OpenMP编程,实现了对动态模式识别的并行计算。通过设置不同的线程数,讨论了几个影响并行程序性能的重要因素。结果表明,要综合考虑各种因素的影响才能设计出高效的并行识别程序。  相似文献
10.
针对一类非线性系统,提出了一种新的故障诊断方法;首先,对未知的系统正常模式和系统故障模式分别进行确定学习,将系统正常模式和各种故障模式以空间分布的常数神经网络权值方式储存,建立模式库;然后,根据已有模式库中的模式构造一系列估计器,将估计器的状态与实际系统状态进行比较,构造残差,以此来检测和分离各种故障;最后,以弹簧减震器系统为例,用仿真结果证明了文中设计的故障诊断方法的可行性和有效性.  相似文献
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