确定学习与基于数据的建模及控制

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.     

Silhouette-based gait recognition via deterministic learning

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.     

Electrocardiogram (ECG) pattern modeling and recognition via deterministic learning

A method for electrocardiogram （ECG） pattern modeling and recognition via deterministic learning theory is presented in this paper. Instead of recognizing ECG signals beat-to-beat, each ECG signal which contains a number of heartbeats is recognized. The method is based entirely on the temporal features （i.e., the dynamics） of ECG patterns, which contains complete information of ECG patterns. A dynamical model is employed to demonstrate the method, which is capable of generating synthetic ECG signals. Based on the dynamical model, the method is shown in the following two phases： the identification （training） phase and the recognition （test） phase. In the identification phase, the dynamics of ECG patterns is accurately modeled and expressed as constant RBF neural weights through the deterministic learning. In the recognition phase, the modeling results are used for ECG pattern recognition. The main feature of the proposed method is that the dynamics of ECG patterns is accurately modeled and is used for ECG pattern recognition. Experimental studies using the Physikalisch-Technische Bundesanstalt （PTB） database are included to demonstrate the effectiveness of the approach.     

Dynamical pattern recognition for univariate time series and its application to an axial compressor

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.     

轴流压气机旋转失速建模与检测:基于确定学习理论与高阶Moore-Greitzer模型的研究

喘振和旋转失速是轴流压气机研究领域中重要而困难的问题. 本文基于确定学习理论及动态模式识别方法提出一个旋转失速初始扰动 近似准确建模和快速检测的方法. 首先,基于高阶Moore-Greitzer模型（Mansoux模型）,利用确定学习理论提出一个对旋转失速初始扰动的内在系统动态的近似准确建模方法;其次,基于以上近似准确建模,利用 动态模式识别方法提出一个对旋转失速初始扰动的快速检测方法. 基于MIT的Mansoux-C2模型仿真研究验证了 所提方法的有效性. 最后,在北京航空航天大学航空发动机重点实验室的低速轴流压气机试验台上开展了试验研究. 通过对低速轴流压气机试验台参数进行测量,得到基于北航低速轴流压气机试验台的Mansoux模型. 通过对基于北航试验台Mansoux模型进行仿真研究,验证了所提方法的有效性.     

Modeling of nonlinear dynamical systems based on deterministic learning and structural stability基于确定学习和结构稳定理论的非线性动态系统建模

Recently, a deterministic learning (DL) theory was proposed for accurate identification of system dynamics for nonlinear dynamical systems. In this paper, we further investigate the problem of modeling or identification of the partial derivative of dynamics for dynamical systems. Firstly, based on the locally accurate identification of the unknown system dynamics via deterministic learning, the modeling of its partial derivative of dynamics along the periodic or periodic-like trajectory is obtained by using the mathematical concept of directional derivative. Then, with accurately identified system dynamics and the partial derivative of dynamics, a C¹-norm modeling approach is proposed from the perspective of structural stability, which can be used for quantitatively measuring the topological similarities between different dynamical systems. This provides more incentives for further applications in the classification of dynamical systems and patterns, as well as the prediction of bifurcation and chaos. Simulation studies are included to demonstrate the effectiveness of this modeling approach.     

Rapid dynamical pattern recognition for sampling sequences

In this paper,based on deterministic learning,we propose a method for rapid recognition of dynamical patterns consisting of sampling sequences.First,for the seq...     

并行计算在动态模式识别中的实现和应用*

研究一种针对最近提出的动态环境下的机器学习理论——确定学习理论的算法实现,提出一种采用并行计算实现确定学习理论中的动态模式识别的方法。利用并行计算中的OpenMP多核编程环境,采用曙光16核服务器为硬件平台,实现对动态模式识别算法的快速性。同时,以压气机Mansoux模型为应用背景,把确定学习理论的动态模式识别方法应用到压气机旋转失速/喘振的快速检测中,利用多核并行计算实现了从包含多种旋转失速/喘振模式的模式库中快速识别当前模式的方法,为文章中方法提供了一个有效的验证。     

Fault detection for nonlinear discrete-time systems via deterministic learning

Recently, an approach for the rapid detection of small oscillation faults based on deterministic learning theory was proposed for continuous-time systems. In this paper, a fault detection scheme is proposed for a class of nonlinear discrete-time systems via deterministic learning. By using a discrete-time extension of deterministic learning algorithm, the general fault functions (i.e., the internal dynamics) underlying normal and fault modes of nonlinear discrete-time systems are locally-accurately approximated by discrete-time dynamical radial basis function (RBF) networks. Then, a bank of estimators with the obtained knowledge of system dynamics embedded is constructed, and a set of residuals are obtained and used to measure the differences between the dynamics of the monitored system and the dynamics of the trained systems. A fault detection decision scheme is presented according to the smallest residual principle, i.e., the occurrence of a fault can be detected in a discrete-time setting by comparing the magnitude of residuals. The fault detectability analysis is carried out and the upper bound of detection time is derived. A simulation example is given to illustrate the effectiveness of the proposed scheme.     

AUV深度的神经网络辨识和学习控制仿真研究

将自主水下航行器（AUV）的深度控制问题转换为对非线性严格反馈系统的分析,提出了一种结合反步法和确定学习理论的自适应学习控制方法。通过反步法设计了一种输入状态稳定（ISS）神经网络控制器,其中引入小增益定理,避免了控制器设计中存在的奇异值问题,并在满足持续激励（PE）条件下,利用神经网络辨识实现了对系统未知动态的局部准确逼近和部分神经网络权值的收敛,保证了闭环系统的稳定。将从动态模式中学到的知识静态保存,提取动态特征设计学习控制器,仿真结果表明,该控制器避免了执行同样任务时的重复训练,改善了系统控制性能,验证了所提控制方法的有效性。     

Design and analysis of a nonequilibrium cross-coupled network witha detectable similarity measure

In this paper, a nonequilibrium network which works as a dynamical associative memory is designed. The design is based on a new similarity measure between any stored pattern and a state of the network. Although conventional similarity measures, such as Hamming distance, direction cosine, and so on, are not detectable in a cross-coupled network, the similarity measure proposed in this paper is. The new similarity measure is employed in our design. The network should include the following properties in its output pattern sequence, so that the dynamics of cross-coupled network may be designed: 1) Stored patterns are frequently associated in the dynamical association. 2) The dynamical association is very robust against variation of distributed parameters. Property 1) is achieved by introducing the next two operation modes with inverse N-shaped function into the dynamics of the proposed network, 1) When the state of the network is close enough to a stored pattern at a time step, the state is forced to evolve at the next time step, 2) The state of the network converges to a stored one while it is not close to any stored patterns. By considering these two operation modes, the frequency of associating stored patterns is increased. The authors emphasize the property 2) which is very important for a silicon implementation of the proposed network. In the silicon implementation, parameters of the network must be represented by transistors, resistors, capacitors, and other electric components which exhibit variation in their characteristics. Thus the second property guarantees the easy silicon implementation of the nonequilibrium network proposed in this paper.     

Autoassociative learning in relaxation labeling networks

We address the problem of training relaxation labeling processes, a popular class of parallel iterative procedures widely employed in pattern recognition and computer vision. The approach discussed here is entirely based on a theory of consistency developed by Hummel and Zucker, and contrasts with a recently introduced learning stratery which can be regarded as heteroassociative, i.e., what is actually learned is the association between patterns rather than the patterns themselves. The proposed learning model is instead autoassociative and involves making a set of training patterns consistent, in the sense rigorously defined by Hummel and Zucker, this implies that they become local attractors of the relaxation labeling dynamical system. The learning problem is formulated in terms of solving a system of linear inequalities, and a straightforward iterative algorithm is presented to accomplish this. The attractive feature of this algorithm is that it solves the system when it admits a solution, and automatically yields the best approximation solution when this is not the case. The learning model described here allows one to view the relaxation labeling process as a kind of asymmetric associative memory, the effectiveness of which is demonstrated experimentally.     

基于确定学习理论和Lempel-Ziv复杂度的非线性系统动态特征提取

对非线性系统产生的非线性非平稳信号进行有效的特征表达是特征提取领域重要且困难的问题.本文基于确定学习理论和Lempel-Ziv复杂度（LZ复杂度）提出一种新的非线性系统动态特征提取方法.新方法将从系统的动力学轨迹中提取特征.通过确定学习理论对产生回归轨迹的非线性动力学系统的未知系统动态进行局部准确建模/辨识,1）使用LZ复杂度对辨识得到的动力学轨迹进行特征表达,并提出时间复杂度和空间复杂度两个指标组成时空LZ复杂度,从时间域和空间域的角度刻画系统动力学轨迹的复杂程度.2）对提出的动态特征提取方法进行敏感度分析,定量评价系统的动态特征指标相对于系统从周期轨迹到混沌轨迹的参数变化敏感程度.3）通过数值仿真和实验分析以验证动态特征提取的有效性.与从系统状态轨迹中提取特征相比,本文提出的动态特征提取方法可以从系统内在动态的角度对原系统进行更好的表达.     

基于动态模式的转子系统故障诊断

以Jeffcott转子系统基础松动-碰摩耦合故障为例,研宄动态模式的转子系统故障诊断方法.首先,将转子系统正常和故障时的未知系统动态定义为不同的动态模式,对其进行学习,将学到的知识以常数神经网络权值的形式存储,并建立动态模式库;然后将当前被监测转子系统与动态模式库中的动态模式进行比较,根据动态模式的相似性定义,依据最小误差原则快速判断转子系统与已学过的哪种动态模式相似,实现故障的快速检测与分离.仿真结果验证了算法的有效性.     

基于机器学习的语音驱动人脸动画方法

语音与唇动面部表情的同步是人脸动画的难点之一.综合利用聚类和机器学习的方法学习语音信号和唇动面部表情之间的同步关系,并应用于基于MEPG-4标准的语音驱动人脸动画系统中.在大规模音视频同步数据库的基础上,利用无监督聚类发现了能有效表征人脸运动的基本模式,采用神经网络学习训练,实现了从含韵律的语音特征到人脸运动基本模式的直接映射,不仅回避了语音识别鲁棒性不高的缺陷,同时学习的结果还可以直接驱动人脸网格.最后给出对语音驱动人脸动画系统定量和定性的两种分析评价方法.实验结果表明,基于机器学习的语音驱动人脸动画不仅能有效地解决语音视频同步的难题,增强动画的真实感和逼真性,同时基于MPEG-4的学习结果独立于人脸模型,还可用来驱动各种不同的人脸模型,包括真实视频、2D卡通人物以及3维虚拟人脸.     

Deterministic learning and nonlinear observer design

A “deterministic learning” (DL) theory was recently proposed for identification of nonlinear system dynamics under full‐state measurements. In this paper, for a class of nonlinear systems undergoing periodic or recurrent motions with only output measurements, firstly, it is shown that locally‐accurate identification of nonlinear system dynamics can still be achieved. Specifically, by using a high gain observer and a dynamical radial basis function network (RBFN), when state estimation is achieved by the high gain observer, along the estimated state trajectory, a partial persistence of excitation (PE) condition is satisfied, and locally‐accurate identification of system dynamics is achieved in a local region along the estimated state trajectory. Secondly, by embedding the learned knowledge of system dynamics into a RBFN‐based nonlinear observer, it is shown that correct state estimation can be achieved according to the internal matching of the underlying system dynamics, rather than by using high gain domination. The significance of this paper is that it reveals that the difficult problems in nonlinear observer design can be successfully resolved by incorporating the deterministic learning mechanisms. Simulation studies are included to demonstrate the effectiveness of the approach. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A Network of Neural Oscillators for Fractal Pattern Recognition

Biological neural networks are high dimensional nonlinear systems, which presents complex dynamical phenomena, such as chaos. Thus, the study of coupled dynamical systems is important for understanding functional mechanism of real neural networks and it is also important for modeling more realistic artificial neural networks. In this direction, the study of a ring of phase oscillators has been proved to be useful for pattern recognition. Such an approach has at least three nontrivial advantages over the traditional dynamical neural networks: first, each input pattern can be encoded in a vector instead of a matrix; second, the connection weights can be determined analytically; third, due to its dynamical nature, it has the ability to capture temporal patterns. In the previous studies of this topic, all patterns were encoded as stable periodic solutions of the oscillator network. In this paper, we continue to explore the oscillator ring for pattern recognition. Specifically, we propose algorithms, which use the chaotic dynamics of the closed loops of Stuart–Landau oscillators as artificial neurons, to recognize randomly generated fractal patterns. We manipulate the number of neurons and initial conditions of the oscillator ring to encode fractal patterns. It is worth noting that fractal pattern recognition is a challenging problem due to their discontinuity nature and their complex forms. Computer simulations confirm good performance of the proposed algorithms.     

Developing Complex Systems Using Evolved Pattern Generators

Self-organization of connection patterns within brain areas of animals begins prenatally, and has been shown to depend on internally generated patterns of neural activity. The neural structures continue to develop postnatally through externally driven patterns, when the sensory systems are exposed to stimuli from the environment. The internally generated patterns have been proposed to give the neural system an appropriate bias so that it can learn reliably from complex environmental stimuli. This paper evaluates the hypothesis that complex artificial learning systems can benefit from a similar approach, consisting of initial training with patterns from an evolved pattern generator, followed by training with the actual training set. To test this hypothesis, competitive learning networks were trained for recognizing handwritten digits. The results demonstrate how the approach can improve learning performance by discovering the appropriate initial weight biases, thereby compensating for weaknesses of the learning algorithm. Due to the smaller evolutionary search space, this approach was also found to require much fewer generations than direct evolution of network weights. Since discovering the right biases efficiently is critical for solving large-scale problems with learning, these results suggest that internal training pattern generation is an effective method for constructing complex systems     

Pattern fusion in feature recognition neural networks forhandwritten character recognition

B. Hussain and M.R. Kabuka (1994) proposed a feature recognition neural network to reduce the network size of neocognitron. However, a distinct subnet is created for every training pattern. Therefore, a big network is obtained when the number of training patterns is large. Furthermore, recognition rate can be hurt due to the failure of combining features from similar training patterns. We propose an improvement by incorporating the idea of fuzzy ARTMAP in the feature recognition neural network. Training patterns are allowed to be merged, based on the measure of similarity among features, resulting in a subnet being shared by similar patterns. Because of the fusion of training patterns, network size is reduced and recognition rate is increased.     

A hybrid immune model for unsupervised structural damage pattern recognition

This paper presents an unsupervised structural damage pattern recognition approach based on the fuzzy clustering and the artificial immune pattern recognition (AIPR). The fuzzy clustering technique is used to initialize the pattern representative (memory cell) for each data pattern and cluster training data into a specified number of patterns. To improve the quality of memory cells, the artificial immune pattern recognition method based on immune learning mechanisms is employed to evolve memory cells. The presented hybrid immune model (combined with fuzzy clustering and the artificial immune pattern recognition) has been tested using a benchmark structure proposed by the IASC–ASCE (International Association for Structural Control–American Society of Civil Engineers) Structural Health Monitoring Task Group. The test results show the feasibility of using the hybrid AIPR (HAIPR) method for the unsupervised structural damage pattern recognition.