共查询到20条相似文献,搜索用时 31 毫秒
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This paper describes an approach to estimating the progress in a task executed by a humanoid robot and to synthesizing motion based on the current progress so that the robot can achieve the task. The robot observes a human performing whole body motion for a specific task, and encodes these motions into a hidden Markov model (HMM). The current observation is compared with the motion generated by the HMM, and the task progress can be estimated during the robot performing the motion. The robot subsequently uses the estimate of the task progress to generate a motion appropriate to the current situation with the feedback rule. We constructed a bilateral remote control system with humanoid robot HRP-4 and haptic device Novint Falcon, and we made the humanoid robot push a button. Ten trial motions of pushing a button were recorded for the training data. We tested our proposed approach on the autonomous execution of the pushing motion by the humanoid robot, and confirmed the effectiveness of our task progress feedback method. 相似文献
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Kwang-Eun Ko Kwee-Bo Sim 《International Journal of Control, Automation and Systems》2013,11(3):608-613
This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks. 相似文献
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Guofeng Wang Xiaoliang Feng 《Engineering Applications of Artificial Intelligence》2013,26(4):1421-1427
Tool condition monitoring (TCM) system is paramount for guaranteeing the quality of workpiece and improving the efficiency of the machining process. To overcome the shortcomings of Hidden Markov Model (HMM) and improve the accuracy of tool wear recognition, a linear chain conditional random field (CRF) model is presented. As a global conditional probability model, the main characteristic of this method is that the estimation of the model parameters depends not only on the current feature vectors but also on the context information in the training data. Therefore, it can depict the interrelationship between the feature vectors and the tool wear states accurately. To test the effectiveness of the proposed method, acoustic emission data are collected under four kinds of tool wear state and seven statistical features are selected to realize the tool wear classification by using CRF and hidden Markov model (HMM) based pattern recognition method respectively. Moreover, k-fold cross validation method is utilized to estimate the generation error accurately. The analysis and comparison under different folds schemes show that the CRF model is more accurate for the classification of the tool wear state. Moreover, the stability and the training speed of the CRF classifier outperform the HMM model. This method casts some new lights on the tool wear monitoring especially in the real industrial environment. 相似文献
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SVM+BiHMM:基于统计方法的元数据抽取混合模型 总被引:3,自引:0,他引:3
提出了一种SVM BiHMM的混合元数据自动抽取方法.该方法基于SVM(support vector machine)和二元HMM(bigram HMM(hidden Markov model),简称BiHMM)理论.二元HMM模型BiHMM在保持模型结构不变的前提下,通过区分首发概率和状态内部发射概率,修改了HMM发射概率计算模型.在SVM BiHMM复合模型中,首先根据规则把论文粗分为论文头、正文以及引文部分,然后建立SVM模型把文本块划分为元数据子类,接着采用Sigmoid双弯曲函数把SVM分类结果用于拟合调整BiHMM模型的单词发射概率,最后用复合模型进行元数据抽取.SVM方法有效考虑了块间联系,BiHMM模型充分考虑了单词在状态内部的位置信息,二者的元数据抽取结果得到了很好的互补和修正,实验评测结果表明,SVM BiHMM算法的抽取效果优于其他方法. 相似文献
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Given recent experimental results suggesting that neural circuits may evolve through multiple firing states, we develop a framework for estimating state-dependent neural response properties from spike train data. We modify the traditional hidden Markov model (HMM) framework to incorporate stimulus-driven, non-Poisson point-process observations. For maximal flexibility, we allow external, time-varying stimuli and the neurons' own spike histories to drive both the spiking behavior in each state and the transitioning behavior between states. We employ an appropriately modified expectation-maximization algorithm to estimate the model parameters. The expectation step is solved by the standard forward-backward algorithm for HMMs. The maximization step reduces to a set of separable concave optimization problems if the model is restricted slightly. We first test our algorithm on simulated data and are able to fully recover the parameters used to generate the data and accurately recapitulate the sequence of hidden states. We then apply our algorithm to a recently published data set in which the observed neuronal ensembles displayed multistate behavior and show that inclusion of spike history information significantly improves the fit of the model. Additionally, we show that a simple reformulation of the state space of the underlying Markov chain allows us to implement a hybrid half-multistate, half-histogram model that may be more appropriate for capturing the complexity of certain data sets than either a simple HMM or a simple peristimulus time histogram model alone. 相似文献
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Christos Ferles Georgios Siolas Andreas Stafylopatis 《Applied Artificial Intelligence》2013,27(6):461-495
The self-organizing hidden Markov model map (SOHMMM) introduces a hybrid integration of the self-organizing map (SOM) and the hidden Markov model (HMM). Its scaled, online gradient descent unsupervised learning algorithm is an amalgam of the SOM unsupervised training and the HMM reparameterized forward-backward techniques. In essence, with each neuron of the SOHMMM lattice, an HMM is associated. The image of an input sequence on the SOHMMM mesh is defined as the location of the best matching reference HMM. Model tuning and adaptation can take place directly from raw data, within an automated context. The SOHMMM can accommodate and analyze deoxyribonucleic acid, ribonucleic acid, protein chain molecules, and generic sequences of high dimensionality and variable lengths encoded directly in nonnumerical/symbolic alphabets. Furthermore, the SOHMMM is capable of integrating and exploiting latent information hidden in the spatiotemporal dependencies/correlations of sequences’ elements. 相似文献
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This paper is concerned with filtering of hidden Markov processes (HMP) which possess (or approximately possess) the property of lumpability. This property is a generalization of the property of lumpability of a Markov chain which has been previously addressed by others. In essence, the property of lumpability means that there is a partition of the (atomic) states of the Markov chain into aggregated sets which act in a similar manner as far as the state dynamics and observation statistics are concerned. We prove necessary and sufficient conditions on the HMP for exact lumpability to hold. For a particular class of hidden Markov models (HMM), namely finite output alphabet models, conditions for lumpability of all HMP representable by a specified HMM are given. The corresponding optimal filter algorithms for the aggregated states are then derived. The paper also describes an approach to efficient suboptimal filtering for HMP which are approximately lumpable. By this we mean that the HMM generating the process may be approximated by a lumpable HMM. This approach involves directly finding a lumped HMM which approximates the original HMM well, in a matrix norm sense. An alternative approach for model reduction based on approximating a given HMM by an exactly lumpable HMM is also derived. This method is based on the alternating convex projections algorithm. Some simulation examples are presented which illustrate the performance of the suboptimal filtering algorithms 相似文献
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针对语音识别系统对抗环境噪声的实际需求,提出一种二次组合抗噪技术,研究并设计了一种以数字信号处理器(DSP)为硬件平台,以隐马尔可夫模型(HMM)为算法的抗噪声嵌入式语音识别系统.DSP采用型号为TMS320VC5509A的芯片,配以外围硬件电路构成语音识别系统的硬件平台.软件设计以离散隐马尔可夫模型(DHMM)为识别算法进行编程,系统软件主要有识别、训练、学习和USB四个主要模块.实验结果表明:基于二次组合去噪技术的语音识别系统有更好的抗噪声效果. 相似文献
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This paper aims to address the problem of modeling human behavior patterns captured in surveillance videos for the application
of online normal behavior recognition and anomaly detection. A novel framework is developed for automatic behavior modeling
and online anomaly detection without the need for manual labeling of the training data set. The framework consists of the
following key components. 1) A compact and effective behavior representation method is developed based on spatial-temporal
interest point detection. 2) The natural grouping of behavior patterns is determined through a novel clustering algorithm,
topic hidden Markov model (THMM) built upon the existing hidden Markov model (HMM) and latent Dirichlet allocation (LDA),
which overcomes the current limitations in accuracy, robustness, and computational efficiency. The new model is a four-level
hierarchical Bayesian model, in which each video is modeled as a Markov chain of behavior patterns where each behavior pattern
is a distribution over some segments of the video. Each of these segments in the video can be modeled as a mixture of actions
where each action is a distribution over spatial-temporal words. 3) An online anomaly measure is introduced to detect abnormal
behavior, whereas normal behavior is recognized by runtime accumulative visual evidence using the likelihood ratio test (LRT)
method. Experimental results demonstrate the effectiveness and robustness of our approach using noisy and sparse data sets
collected from a real surveillance scenario. 相似文献
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一种改进的隐马尔可夫模型在语音识别中的应用 总被引:1,自引:0,他引:1
提出了一种新的马尔可夫模型——异步隐马尔可夫模型.该模型针对噪音环境下语音识别过程中出现丢失帧的情况,通过增加新的隐藏时间标示变量Ck,估计出实际观察值对应的状态序列,实现对不规则或者不完整采样数据的建模.详细介绍了适合异步HMM的前后向算法以及用于训练的EM算法,并且对转移矩阵的计算进行了优化.最后通过实验仿真,分别使用经典HMM和异步HMM对相同的随机抽取帧的语音数据进行识别,识别结果显示在抽取帧相同情况下异步HMM比经典HMM的识别错误率低. 相似文献
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Asymptotical statistics of misspecified hidden Markov models 总被引:1,自引:0,他引:1
This paper deals with the problem of modeling data generated by an ergodic stochastic process as the output of a hidden Markov model (HMM). More specifically, we consider the problem of fitting a parametric family of HMM with continuous output to an ergodic stochastic process with continuous values, which does not necessarily belong to the family. In this context, we derive the main asymptotic results: almost sure consistency of the maximum likelihood estimator, asymptotic normality of the estimation error and the exact rates of almost sure convergence. 相似文献
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J.-F. Mari F. Le Ber 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2006,10(5):406-414
In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov
models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states
depend on the n previous states according to the order of the model. We study the process of achieving information extraction
from spatial and temporal data by means of an unsupervised classification. We use therefore a French national database related
to the land use of a region, named Ter Uti, which describes the land use both in the spatial and temporal domain. Land-use
categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a
temporal sequence of images in which we look for spatial and temporal dependencies.
The temporal segmentation of the data is done by means of a second-order Hidden Markov Model (HMM2) that appears to have very
good capabilities to locate stationary segments, as shown in our previous work in speech recognition. The spatial classification
is performed by defining a fractal scanning of the images with the help of a Hilbert–Peano curve that introduces a total order
on the sites, preserving the relation of neighborhood between the sites. We show that the HMM2 performs a classification that
is meaningful for the agronomists.
Spatial and temporal classification may be achieved simultaneously by means of a two levels HMM2 that measures the a posteriori
probability to map a temporal sequence of images onto a set of hidden classes. 相似文献
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In this paper, we introduce a Hidden Markov Model (HMM) for studying an optimal investment problem of an insurer when model uncertainty is present. More specifically, the financial price and insurance risk processes are modulated by a continuous‐time, finite‐state, hidden Markov chain. The states of the chain represent different modes of the model. The HMM approach is viewed as a ‘dynamic’ version of the Bayesian approach to model uncertainty. The optimal investment problem is formulated as a stochastic optimal control problem with partial observations. The innovations approach in the filtering theory is then used to transform the problem into one with complete observations. New robust filters of the chain and estimates of key parameters are derived. We discuss the optimal investment problem using the Hamilton–Jacobi–Bellman (HJB) dynamic programming approach and derive a closed‐form solution in the case of an exponential utility and zero interest rate. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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入侵检测是网络安全领域的研究热点,协议异常检测更是入侵检测领域的研究难点.提出一种新的基于隐Markov模型(HMM)的协议异常检测模型.这种方法对数据包的标志位进行量化,得到的数字序列作为HMM的输入,从而对网络的正常行为建模.该模型能够区分攻击和正常网络数据.模型的训练和检测使用DARPA1999年的数据集,实验结果验证了所建立模型的准确性,同现有的基于Markov链(Markov chain)的检测方法相比,提出的方法具有较高的检测率. 相似文献