Two-Stage Method for Diagonal Recurrent Neural Network Identification of a High-Power Continuous Microwave Heating System

This paper proposes a diagonal recurrent neural network (DRNN) based identification scheme to handle the complexity and nonlinearity of high-power continuous microwave heating system (HPCMHS). The new DRNN design involves a two-stage training process that couples an efficient forward model selection technique with gradient-based optimization. In the first stage, an impact recurrent network structure is obtained by a fast recursive algorithm in a stepwise forward procedure. To ensure stability, update rules are further developed using Lyapunov stability criterion to tune parameters of reduced size model at the second stage. The proposed approach is tested with an experimental regression problem and a practical HPCMHS identification, and the results are compared with four typical network models. The results show that the new design demonstrates improved accuracy and model compactness with reduced computational complexity over the existing methods.

     

基于正则化路径的支持向量机近似模型选择

模型选择问题是支持向量机的基本问题.基于核矩阵近似计算和正则化路径,提出一个新的支持向量机模型选择方法.首先,发展初步的近似模型选择理论,包括给出核矩阵近似算法KMA-α,证明KMA-α的近似误差界定理,进而得到支持向量机的模型近似误差界.然后,提出近似模型选择算法AMSRP.该算法应用KMA-α计算的核矩阵的低秩近似来提高支持向量机求解的效率,同时应用正则化路径算法来提高惩罚因子C参数调节的效率.最后,通过标准数据集上的对比实验,验证了AMSRP的可行性和计算效率.实验结果显示,AMSRP可在保证测试集准确率的前提下,显著地提高支持向量机模型选择的效率.理论分析与实验结果表明,AMSRP是一合理、高效的模型选择算法.     

Computational system identification for Bayesian NARMAX modelling

In this contribution we derive a computational Bayesian approach to NARMAX model identification. The identification algorithm exploits continuing advances in computational processing power to numerically obtain posterior distributions for both model structure and parameters via sampling methods. The main advantage of this approach over other NARMAX identification algorithms is that for the first time model uncertainty is characterised as a byproduct of the identification procedure. The algorithm is based on the reversible jump Markov chain Monte Carlo (RJMCMC) procedure. Key features of the approach are (i) sampling of unselected model terms for testing for inclusion in the model (the birth move), which encourages global searching of the model term space, (ii) sampling of previously selected model terms for testing for exclusion from the model—a naturally incorporated pruning step (the death move), which leads to model parsimony, and (iii) estimation of model and parameter distributions, which are naturally generated in the Bayesian framework. We present a numerical example to demonstrate the algorithm and a comparison with a forward regression method: the results show that the RJMCMC approach is competitive and gives useful additional information regarding uncertainty in both model parameters and structure.     

Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

The main difficulty with EM algorithm for mixture model concerns the number of components, say g. This is the question of model selection, and the EM algorithm itself could not estimate g. On the contrary, the algorithm requires g to be specified before the remaining parameters can be estimated. To solve this problem, a new algorithm, which is called stepwise split-and-merge EM (SSMEM) algorithm, is proposed. The SSMEM algorithm alternately splits and merges components, estimating g and other parameters of components simultaneously. Also, two novel criteria are introduced to efficiently select the components for split or merge. Experimental results on simulated and real data demonstrate the effectivity of the proposed algorithm.     

Model reduction of uncertain systems retaining the uncertainty structure

Model reduction of high order linear-in-parameters discrete-time systems is considered. The main novelty of the paper is that the coefficients of the original system model are assumed to be known only within given intervals, and the coefficients of the derived reduced order model are also obtained in intervals, such that the complex value sets of the uncertain original and reduced models will be optimally close to each other on the unit circle. The issue of inclusion of one value set in another is also addressed in the paper. The meaning of model reduction is defined for linear-in-parameters systems. The algorithm for obtaining the value sets of such systems is derived in the paper. Then, applying a novel approach, the infinity norm of “distance” between two polygons representing the original and the reduced uncertain systems is minimized. A noteworthy point is that by a special definition of this distance the problem is formulated as a linear semi-infinite programming problem with linear constraints, thus reducing significantly the computational complexity. Numerical example is provided.     

Fast combinatorial vector field topology

This paper introduces a novel approximation algorithm for the fundamental graph problem of combinatorial vector field topology (CVT). CVT is a combinatorial approach based on a sound theoretical basis given by Forman's work on a discrete Morse theory for dynamical systems. A computational framework for this mathematical model of vector field topology has been developed recently. The applicability of this framework is however severely limited by the quadratic complexity of its main computational kernel. In this work, we present an approximation algorithm for CVT with a significantly lower complexity. This new algorithm reduces the runtime by several orders of magnitude and maintains the main advantages of CVT over the continuous approach. Due to the simplicity of our algorithm it can be easily parallelized to improve the runtime further.     

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness     

一种新的全局运动估计算法

全局运动估计是计算机视觉、视频处理等领域广泛采用的手段之一。该文提出一种直接利用压缩视频码流进行全局运动估计的新算法。就运动模型而言,采用了复杂性与准确性较好均衡的六参数仿射模型。为了提高估计精度和计算效率,文章首先提出一种新的视频背景前景分割方法,基于分割的结果,采用背景宏块的运动矢量进行全局运动参数估计。然后,根据参数估计误差的统计特性,一部分运动矢量会当作局外的样值而被剔除,利用剩余的运动矢量中重新估计参数可以提高估计精度。实验验证了提出的全局运动估计算法的计算效率和精度。     

Speeding up the evaluation phase of GP classification algorithms on GPUs

The efficiency of evolutionary algorithms has become a studied problem since it is one of the major weaknesses in these algorithms. Specifically, when these algorithms are employed for the classification task, the computational time required by them grows excessively as the problem complexity increases. This paper proposes an efficient scalable and massively parallel evaluation model using the NVIDIA CUDA GPU programming model to speed up the fitness calculation phase and greatly reduce the computational time. Experimental results show that our model significantly reduces the computational time compared to the sequential approach, reaching a speedup of up to 820×. Moreover, the model is able to scale to multiple GPU devices and can be easily extended to any evolutionary algorithm.     

高效的图模型点模式匹配算法

点模式匹配的概率图模型具有很好的匹配精度,但是计算复杂度较高,当隔离子中包含异常点（outlier）时匹配精度会受到较大的影响。为了提高匹配的速度和精度,提出了一种由粗到精的图模型点模式匹配算法。利用包含特征点的窗口,用标准化互相关方法对特征点进行粗匹配,以减少异常点的数量,提高后续匹配方法的速度和精度。提出了一种新的点模式匹配的概率图模型,这种图模型能综合利用特征点的位置信息和包含特征点的邻域的灰度信息。利用提出的概率图匹配方法对粗匹配所得到的点对进行分段匹配,得到精确的匹配结果。对光学图像和遥感图像的匹配实验显示该方法能显著减少点模式匹配时间,提高匹配的精度。     

Model order selection of nonlinear input–output models––a clustering based approach

Selecting the order of an input–output model of a dynamical system is a key step toward the goal of system identification. The false nearest neighbors algorithm (FNN) is a useful tool for the estimation of the order of linear and nonlinear systems. While advanced FNN uses nonlinear input–output data-based models for the model-based selection of the threshold constant that is used to compute the percentage of false neighbors, the computational effort of the method increases along with the number of data and the dimension of the model. To increase the efficiency of this method, in this paper we propose a clustering-based algorithm. Clustering is applied to the product space of the input and output variables. The model structure is then estimated on the basis of the cluster covariance matrix eigenvalues. The main advantage of the proposed solution is that it is model-free. This means that no particular model needs to be constructed in order to select the order of the model, while most other techniques are ‘wrapped' around a particular model construction method. This saves the computational effort and avoids a possible bias due to the particular construction method used. Three simulation examples are given to illustrate the proposed technique: estimation of the model structure for a linear system, a polymerization reactor and the van der Vusse reactor.     

H.264/AVC快速帧间模式选择研究

在帧间预测编码过程中,常采用多种模式和多参考帧进行精确的运动估计,使用穷举搜索模式选择算法获取最佳模式,提高H.264/AVC的率失真性能,但穷举搜索的计算量非常大.该文研究了快速模式选择算法,改进了H.264时间效率、提高了实时性,提出了模式选择优化算法的基本思路,讨论了几种有代表性的快速帧间模式选择算法,分析了各种快速算法的优化依据和性能,探讨了快速帧间模式选择算法的研究方向.     

Computation of controllability and observability Gramians in modeling of discrete‐time noncommensurate fractional‐order systems

This paper presents a new algorithm for computation of controllability and observability Gramians for an expanded state space form of integer‐order approximator to linear time‐invariant discrete‐time noncommensurate fractional‐order systems. The introduced methodology can significantly reduce the time complexity of the Gramians' calculation, being the main computational burden in modeling of discrete‐time fractional‐order systems by means of a high integer‐order expanded state space approximator and the balanced truncation reduction method. Simulation experiments illustrate an efficiency of the introduced methodology, in particular for low‐dimension fractional‐order systems and high implementation lengths.     

Nash-optimization enhanced distributed model predictive control applied to the Shell benchmark problem

This paper presents an efficient distributed model predictive control scheme based on Nash optimality, in which the on-line optimization of the whole system is decomposed into that of several small co-operative agents in distributed structures, thus it can significantly reduce computational complexity in model predictive control of large-scale systems. The relevant nominal stability and the performance on single-step horizon under the communication failure are investigated. The Shell heavy oil fractionator benchmark control problem is illustrated to verify the effectiveness of the proposed control algorithm.     

Improved feature selection algorithm with fuzzy-rough sets on compact computational domain

The aim of this paper is to provide an efficient input feature selection algorithm for modeling of systems based on modified definition of fuzzy-rough sets. Some of the critical issues concerning the complexity and convergence of the feature selection algorithm are discussed in detail. Based on some natural properties of fuzzy t-norm and t-conorm operators, the concept of fuzzy-rough sets on compact computational domain is put forward, which is then utilized to construct improved Fuzzy-Rough Feature Selection algorithm. Various mathematical properties of this new definition of fuzzy-rough sets are discussed from pattern classification viewpoint. Speedup factor as high as 622 has been achieved with proposed algorithm compared to recently proposed FRSAR, with improved model performance on selected set of features.     

Towards improving cluster-based feature selection with a simplified silhouette filter

This paper proposes a filter-based algorithm for feature selection. The filter is based on the partitioning of the set of features into clusters. The number of clusters, and consequently the cardinality of the subset of selected features, is automatically estimated from data. The computational complexity of the proposed algorithm is also investigated. A variant of this filter that considers feature-class correlations is also proposed for classification problems. Empirical results involving ten datasets illustrate the performance of the developed algorithm, which in general has obtained competitive results in terms of classification accuracy when compared to state of the art algorithms that find clusters of features. We show that, if computational efficiency is an important issue, then the proposed filter may be preferred over their counterparts, thus becoming eligible to join a pool of feature selection algorithms to be used in practice. As an additional contribution of this work, a theoretical framework is used to formally analyze some properties of feature selection methods that rely on finding clusters of features.     

基于局部聚类的自适应线性近邻传递分类算法

针对线性近邻传递(LNP)分类算法中,由于图像过大时计算复杂度高,以及近邻数目选择不当导致图像分类结果不精确的问题,提出了基于局部聚类的自适应LNP分类算法。该方法对LNP分类算法的改进主要体现在两方面,首先运用quick shift进行局部聚类,得到点簇集,以此点簇集作为建图节点,达到缩小矩阵规模的目的;其次,采用测地距离和欧氏距离之间的关系来动态确定每个点的近邻数。实验结果表明,所提方法在得到较好的分类结果的同时,也极大地缩短了运行时间,提高了效率。     

移动音频带宽扩展算法计算复杂度优化

移动通信设备由于计算资源有限,对计算复杂度较为敏感。我国自主研发提出的移动音频编解码算法标准AVS P10中的带宽扩展算法有利于提高移动音频编解码质量,但其计算复杂度较高,不利于该编解码算法在移动环境中应用。通过分析该带宽扩展算法的流程,发现其计算复杂度较高的主要原因是时频变换次数过多,为此从算法和代码两个方面对该算法进行优化:算法方面通过减少快速傅里叶变换(FFT)次数来降低算法计算复杂度;而代码方面则通过空间换时间等方法来减少算法消耗时间。测试结果表明,在不降低整体音频编解码主观质量的情况下,带宽扩展模块在编码端和解码端运行时间比例分别降低了4.5和14.3个百分点,算法计算复杂度显著降低,这有利于进一步在移动音频领域推广应用该编解码算法。     

基于PatchMatch的半全局高效双目立体匹配算法

近年来双目立体匹配技术发展迅速,高精度、高分辨率、大视差的应用需求无疑对该技术的计算效率提出了更高的要求。由于传统立体匹配算法固有的计算复杂度正比于视差范围,已经难以满足高分辨率、大视差的应用场景。因此,从计算复杂度、匹配精度、匹配原理等多方面综合考虑,提出了一种基于PatchMatch的半全局双目立体匹配算法,在路径代价计算过程中使用空间传播机制,将可能的视差由整个视差范围降低为t个候选视差(t远远小于视差范围),显著减少了候选视差的数量,大幅提高了半全局算法的计算效率。对KITTI2015数据集的评估结果表明,该算法以5.81%的错误匹配率和20.2 s的匹配时间实现了准确性和实时性的明显提高。因此,作为传统立体匹配改进算法,该设计可以为大视差双目立体匹配系统提供高效的解决方案。     

一种基于码本聚类的低复杂度用户选择算法

现有块对角化(BD)预编码系统用户选择算法较少考虑利用已选用户与剩余用户之间的关系来排除不可选用户.针对这点不足,给出了一种采用码本聚类的低复杂度用户选择算法( CodeGreedy算法).该算法采用弦距离刻画用户信道的相关性,以此为依据将用户划分到不同码本空间中,聚集在同一码本空间的用户信道具有较强的相关性,形成互斥...