基于模糊推理系统的非线性组合建模与预测方法研究

基于模糊推理系统在紧支集中能够逼近任意非线性连续函数的特性,提出了一种基于Takagi-sugeno模糊规则基的非线性组合建模与预测新方法,以克服线性组合预测方法在解决非平衡时间序列组合建模问题所遇到的困难和存在的不足,并给出了相应的基于学习自动机层次结构的优化算法确定模糊系统的参数和模糊子集的划分,理论分析和大量的经济预测实例表明：该方法具有很强的学习与泛化能力,在处理诸如经济时间序列这种具有一定程度不确定性的非线性系统组合建模与预测方法有很好的应用。     

Texture-independent recognition of facial expressions in image snapshots and videos

This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines.     

Data reduction in headspace analysis of blood and urine samples for robust bacterial identification

This paper demonstrates the application of chemical headspace analysis to the problem of classifying the presence of bacteria in biomedical samples by using computational tools. Blood and urine samples of disparate forms were analysed using a Cyrano Sciences C320 electronic nose together with an Agilent 4440 Chemosensor. The high dimensional data sets resulting from these devices present computational problems for parameter estimation of discriminant models. A variety of data reduction and pattern recognition techniques were employed in an attempt to optimise the classification process. A 100% successful classification rate for the blood data from the Agilent 4440 was achieved by combining a Sammon mapping with a radial basis function neural network. In comparison a successful classification rate of 80% was achieved for the urine data from the C320 which were analysed using a novel nonlinear time series model.     

基于分类器集成的核爆地震模式识别

分类器集成是一种解决复杂模式识别问题的有效方法,它通过形成一组分类器并将它们的结果进行组合,可以显著地提高分类系统的泛化推广能力。论文将分类器集成技术用于核爆地震信号的模式识别,并以前馈神经网络作基分类器为例,研究了不同的分类器个体生成方法和决策形成规则对识别效果的影响。     

Fractal tidal waves in coastal aquifers induced both anthropogenically and naturally

In this paper, we present a methodology developed for quantifying fractal properties of salt concentration profiles induced anthropogenically in coastal aquifers of Bundaberg, Queensland, Australia. Based on data observed in the field and multifractal models, it is found that salt profiles induced in the coastal aquifers are periodic fractals, and the fractal dimensions of the salt profiles and associated tidal waves are predominant in urban and industrial areas where frequent human activities impose severe impact on the waveforms. It is found that the fractal tidal waves are heterogeneous as indicated by variable dimensions of the waveforms at different orders. It is also shown that the results from multifractal analysis are more consistent and reliable than those from spectral analysis. The methods developed in this paper can also be applied to characterise time series data in other fields such as data on hydrographs, water levels, pollutant and air quality etc.     

Incorporating linear discriminant analysis in neural tree for multidimensional splitting

In this paper, a new hybrid classifier is proposed by combining neural network and direct fractional-linear discriminant analysis (DF-LDA). The proposed hybrid classifier, neural tree with linear discriminant analysis called NTLD, adopts a tree structure containing either a simple perceptron or a linear discriminant at each node. The weakly performing perceptron nodes are replaced with DF-LDA in an automatic way. Taking the advantage of this node substitution, the tree building process converges faster and avoids the over-fitting of complex training sets in training process resulting a shallower tree together with better classification performance. The proposed NTLD algorithm is tested on various synthetic and real datasets. The experimental results show that the proposed NTLD leads to very satisfactory results in terms of tree depth reduction as well as classification accuracy.     

Hybrid neural network models for hydrologic time series forecasting

The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.     

模型检测新技术研究

1 引言软件是否可信赖已成为一个国家的经济、国防等系统能否正常运转的关键因素之一,尤其在一些诸如核反应堆控制、航空航天以及铁路调度等安全悠关(safety-critical)领域更是如此。这类系统要求绝对安全可靠,不容半点疏漏,否则将导致灾难性后果。如1996年6月4日,欧洲航天局阿丽亚娜(Ariane)501火箭因为其控制软件的规范和设计错误而导致发射37秒后爆炸。类似的报道屡见不鲜,如何确保这些系统的可靠性成为计算机科学与控制论领域共同关注的一个焦点问题。     

一种进化RBF神经网络的模型及其训练算法

径向基函数神经网络（RBFNN）具有最优逼近和全局逼近的特性,在函数似合方法优于传统的BP网络,因此被广泛应用于非线性时间序列分析算法领域。本文针对时间序列中的非平稳数据,结合差分平稳化与分阶遗传的思想,提出一个新的进化RBF神经网络的模型及其训练算法。通过实例分析表明,该方法在处理非平稳时间序列方面具有一定的优越性。     

Feature extraction of the first difference of EMG time series for EMG pattern recognition

This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2–8%.     

基于时点分割的核Fisher判别分析–顺序回归机多类分类建模方法

针对多维时间序列的多类分类问题,本文提出基于时点分割思想的核Fisher判别分析-顺序回归机(KFDA-ORM)多类分类建模方法.该方法利用核Fisher判别分析(KFDA)与顺序回归机(ORM)的互补性得到分类决策函数;对分类样本的多维时间序列进行时点分割处理,使用决策函数得到各时点的分类级别;通过指数平滑分析得到采样周期内样本的最终分类结果.通过实例验证,该方法对多维时间序列的分类具有较好效果,是一种有效的多类分类方法.     

Performing automatic target detection with evolvable finite state automata

In this paper, a novel approach for performing classification is presented. Discriminant functions are constructed by combining selected features from the feature set with simple mathematical functions such as +, −, ×, ÷ , max, min. These discriminant functions are capable of forming non-linear discontinuous hypersurfaces. For multimodal data, more than one discriminant function may be combined with logical operators before classification is performed. An algorithm capable of making decisions as to whether a combination of discriminant functions is needed to classify a data sample, or whether a single discriminant function will suffice, is developed. The algorithms used to perform classification are not written by a human. The algorithms are learnt, or rather evolved, using evolutionary computing techniques.     

Fisher Difference Discriminant Analysis: Determining the Effective Discriminant Subspace Dimensions for Face Recognition

In most manifold learning based subspace discriminant analysis algorithms, how to construct the local neighborhood graphs and determine the effective discriminant subspace dimensions in applications are difficult but important problems. In this paper, we propose a novel supervised subspace learning method called Fisher Difference Discriminant Analysis (FDDA) for linear dimensionality reduction. FDDA introduces the local soft scatter to characterize the distributions of the data set. By combining Fisher criterion and difference criterion together, FDDA obtains the optimal discriminant subspace, on which a large margin between different classes is provided for classification. Eigenvalue analysis shows that the effective discriminant subspace dimensions of FDDA can be automatically determined by the number of positive eigenvalues and are robust to noise and invariant to rotations, rescalings and translations of the data. Comprehensive comparison and extensive experiments show that FDDA is superior to some state-of-the-art techniques in face recognition.     

Bagging, Boosting and the Random Subspace Method for Linear Classifiers

Recently bagging, boosting and the random subspace method have become popular combining techniques for improving weak classifiers. These techniques are designed for, and usually applied to, decision trees. In this paper, in contrast to a common opinion, we demonstrate that they may also be useful in linear discriminant analysis. Simulation studies, carried out for several artificial and real data sets, show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for critical training sample sizes. Finally, a table describing the possible usefulness of the combining techniques for linear classifiers is presented. Received: 03 November 2000, Received in revised form: 02 November 2001, Accepted: 13 December 2001     

Solution of unsteady Euler equations: Gust-cascade interaction tones

The problem of interaction of a vortical gust with a two-dimensional cascade is considered. Full nonlinear time dependent Euler equations governing the flow are solved employing a 6th-order accurate spatial differencing scheme and a 4th-order accurate time marching technique. The vortical gust is represented by a Fourier series which includes three harmonics. The acoustic response of the cascade for single and multi frequency (vortical) excitations are calculated. The solutions show the generation and propagation of modes that are expected from the theory. It is demonstrated that at low amplitudes of excitation, the time domain analysis produces characteristics of the propagating modes such as the complex mode amplitudes, phase variations, axial waveforms, and tangential waveforms that are in very good agreement with those expected from the linear theory. The exponential decay of the cutoff modes of the first harmonic is also clearly observed. The sound pressure levels of the propagating modes obtained from the present nonlinear time domain analysis are compared with the results of a linearized Navier-Stokes solution and a linearized Euler solution (frequency domain analyses) and good agreement between the results is observed for all the propagating modes.     

子空间半监督Fisher判别分析

Fisher判别分析寻找一个使样本数据类间散度与样本数据类内散度比值最大的子空间, 是一种很流行的监督式特征降维方法. 标注样本数据所属的类别通常需要大量的人工, 消耗大量的时间, 付出昂贵的成本. 为了解决同时利用有类别信息的样本数据和没有类别信息的样本数据用于寻找降维子空间的问题, 我们提出了一种子空间半监督Fisher判别分析方法. 子空间半监督Fisher判别分析寻找这样一个子空间, 这个子空间即保留了从有类别信息的样本数据中学习的类别判别结构, 也保留了从有类别信息的样本数据和没有类别信息的样本数据中学习的样本结构信息. 我们还推导了基于核的子空间半监督Fisher判别分析方法. 通过人脸识别实验验证了本文算法的有效性.     

Single subject image analysis using the complex general linear model--an application to functional magnetic resonance imaging with multiple inputs

A linear time invariant model is applied to functional fMRI blood flow data. Based on traditional time series analysis, this model assumes that the fMRI stochastic output sequence can be determined by a constant plus a linear filter (hemodynamic response function) of several fixed deterministic inputs and an error term assumed stationary with zero mean. The input function consists of multiple exponential distributed (time delay between images) visual stimuli consisting of negative and erotic images. No a priori assumptions are made about the hemodynamic response function that, in essence, is calculated at each spatial position from the data. The sampling rate for the experiment is 400 ms in order to allow for filtering out higher frequencies associated with the cardiac rate. Since the statistical analysis is carried out in the Fourier domain, temporal correlation problems associated with inference in the time domain are avoided. This formal model easily lends itself to further development based on previously developed statistical techniques.     

基于连续密度隐马尔可夫的时间序列分类算法

针对数据挖掘过程中对异常数据检测的准确率较低、分类速度较慢,导致数据分类准确率较低、效率较差的问题,提出基于连续密度隐马尔可夫的时间序列分类算法.构建时间序列变化趋势分割点目标函数,利用贪婪搜索法求解时间序列分段值,提取序列变化趋势特征得到数据主要信息,提升数据分类的准确性;改进帧内特征表达准确性,使用因子分析矩阵高斯...     

An integrated fuzzy time series forecasting system

A number of fuzzy time series models have been designed and developed during the last decade. One problem of these models is that they only provide a single-point forecasted value just like the output of the crisp time series methods. In addition, these models are suitable for forecasting stationary or trend time series, but they are not appropriate for forecasting seasonal time series. Hence, the objective of this study is to develop an integrated fuzzy time series forecasting system in which the forecasted value will be a trapezoidal fuzzy number instead of a single-point value. Furthermore, this system can effectively deal with stationary, trend, and seasonal time series and increase the forecasting accuracy. Two numerical data sets are selected to illustrate the proposed method and compare the forecasting accuracy with four fuzzy time series methods. The results of the comparison show that our system can produce more precise forecasted values than those of four methods.     

基于时间序列的中亚地区云量特征分类及云量变化趋势

利用1984~2009年国际卫星云气候学计划(ISCCP)D2数据集的平均云量(MCA)资料,提取了中亚地区不同区域平均云量的时间序列特征,并利用平均云量时间序列平稳性、季节性等特征将中亚地区云量划分为3类:月平均云量时间序列季节性非平稳类型、月平均云量时间序列普通非平稳类型和月平均云量时间序列平稳类型。从分类结果看,中亚地区云量总体呈现非平稳特征。月平均云量时间序列季节非平稳类型云量主要集中在哈萨克斯坦和新疆地区,月平均云量时间序列普通非平稳类型云量主要分布在哈萨克斯坦北部和中部偏东一直延伸到新疆的条带地区,月平均云量时间序列平稳性类型云量主要分布在阿姆河流域。从平均云量结果看,中亚地区1984~2009年的总体平均云量为 64.69%,最高为1984年的66.50%,最低为1999年的62.38%,最高和最低平均云量之间相差4.12%。中亚地区平均云量变化趋势为先下降后上升,并在中间发生了一次震荡,但总体呈现出下降的趋势。