区间分割共轭梯度混沌优化算法及其应用

本文提出一种区间分割共轭梯度混沌优化算法(CSCGCOA)。新算法首先在全局搜索阶段采用混沌优化算法寻找一个次优解,寻优过程使用区间分割策略。进而以次优解为初值,局部搜索采用共轭梯度算法获得全局最优解。通过针对不同测试函数的仿真,并对比另外两个算法,结果表明新算法对初值不敏感,能有效得到全局最优解,同时具有很高的寻优速度。本文还将新算法应用于解决电力系统经济负荷分配问题,结果表明新算法是一种有效的高速算法。     

ISOETRP clustering algorithm and its application to tree classifier design

A new clustering algorithm ISOETRP has been developed. Several new objectives have been introduced to make ISOETRP particularly suitable to hierarchical pattern classification. These objectives are: a) minimizing overlap between pattern class groups, b) maximizing entropy reduction, and c) keeping balance between these groups. The overall objective to be optimized is GAIN=Entropy Reduction/(Overlap+1). Balance is controlled by maximizing the GAIN. An interactive version of ISOETRP has also been developed by means of an overlap table. It has been shown that ISOETRP gives much better results than other existing algorithms in optimizing the above objectives. ISOETRP has played an important role in designing many large tree classifiers, where the tree performance was improved by optimizing GAIN value.     

基于共轭梯度的罚项最优脑外科过程优化算法

针对最优脑外科过程网络剪枝计算复杂度高,且需要与训练算法配合使用的不足,提出了把剪枝条件以罚项的形式纳入神经网络的训练目标函数中,构建了罚项最优脑外科过程的计算模型.该模型理论上具有收敛性.在此基础上,借助共轭梯度方法,实现了该计算模型.仿真实验结果表明了该算法的有效性.该算法实现了网络训练过程和最优脑外科过程的并行,既保持了最优脑外科过程的准确性,有具有正则化的高效性,提高了神经网络的泛化性能.     

A preconditioned conjugate gradient algorithm for GeneRank with application to microarray data mining

The problem of identifying key genes is of fundamental importance in biology and medicine. The GeneRank model explores connectivity data to produce a prioritization of the genes in a microarray experiment that is less susceptible to variation caused by experimental noise than the one based on expression levels alone. The GeneRank algorithm amounts to solving an unsymmetric linear system. However, when the matrix in question is very large, the GeneRank algorithm is inefficient and even can be infeasible. On the other hand, the adjacency matrix is symmetric in the GeneRank model, while the original GeneRank algorithm fails to exploit the symmetric structure of the problem in question. In this paper, we discover that the GeneRank problem can be rewritten as a symmetric positive definite linear system, and propose a preconditioned conjugate gradient algorithm to solve it. Numerical experiments support our theoretical results, and show superiority of the novel algorithm.     

A modified gradient-based neuro-fuzzy learning algorithm and its convergence

Neuro-fuzzy approach is known to provide an adaptive method to generate or tune fuzzy rules for fuzzy systems. In this paper, a modified gradient-based neuro-fuzzy learning algorithm is proposed for zero-order Takagi-Sugeno inference systems. This modified algorithm, compared with conventional gradient-based neuro-fuzzy learning algorithm, reduces the cost of calculating the gradient of the error function and improves the learning efficiency. Some weak and strong convergence results for this algorithm are proved, indicating that the gradient of the error function goes to zero and the fuzzy parameter sequence goes to a fixed value, respectively. A constant learning rate is used. Some conditions for the constant learning rate to guarantee the convergence are specified. Numerical examples are provided to support the theoretical findings.     

Artificial wavelet neural network and its application in neuro-fuzzy models

In the proposed work, two types of artificial neural networks are proposed by using well-known advantages and valuable features of wavelets and sigmoidal activation functions. Two neurons are derived by adding and multiplying the outputs of the wavelet and the sigmoidal activation functions. These neurons in a feed-forward single hidden layer network result summation wavelet neural network (SWNN) and multiplication wavelet neural network (MWNN). An algorithm is introduced for structure determination of the proposed networks. Approximation properties of SWNN and MWNN have been evaluated with different wavelet functions. The above networks in the consequent part of the neuro-fuzzy model result summation wavelet neuro-fuzzy (SWNF) and multiplication wavelet neuro-fuzzy (MWNF) models. Different types of wavelet function are tested with the proposed networks and fuzzy models on four different dynamical examples. Convergence of the learning process is also guaranteed by adaptive learning rate and performing stability analysis using Lyapunov function.     

Scaled conjugate gradient and Bayesian training of neural networks for fault identification in cylinders

This paper compares Bayesian training of neural networks using hybrid Monte Carlo to scaled conjugate gradient method for fault identification in cylinders using vibration data. From the measured data pseudo-modal energies and modal properties are calculated and the coordinate pseudo-modal energy assurance criterion (COMEAC) and the coordinate modal assurance criterion (COMAC) are computed respectively. The pseudo-modal energies, modal properties, COMEAC and COMAC are used to train four neural networks. On average, the pseudo-modal-energy-networks are more accurate than the modal-property-networks. The weighted averages of the pseudo-modal-energy- and modal-property-networks form a committee of networks. The committee method gives lower mean squared errors and better classification of faults than the individual methods. The Bayesian training is found to be more accurate and computationally expensive than the scaled conjugate gradient method and to give confidence levels.     

A fast neural net training algorithm and its application to speech classification

This paper describes a fast training algorithm for feedforward neural nets, as applied to a two-layer neural network to classify segments of speech as voiced, unvoiced, or silence. The speech classification method is based on five features computed for each speech segment and used as input to the network. The network weights are trained using a new fast training algorithm which minimizes the total least squares error between the actual output of the network and the corresponding desired output. The iterative training algorithm uses a quasi-Newtonian error-minimization method and employs a positive-definite approximation of the Hessian matrix to quickly converge to a locally optimal set of weights. Convergence is fast, with a local minimum typically reached within ten iterations; in terms of convergence speed, the algorithm compares favorably with other training techniques. When used for voiced-unvoiced-silence classification of speech frames, the network performance compares favorably with current approaches. Moreover, the approach used has the advantage of requiring no assumption of a particular probability distribution for the input features.     

A 2D algorithm with asymmetric workload for the UPC conjugate gradient method

This paper examines four different strategies, each one with its own data distribution, for implementing the parallel conjugate gradient (CG) method and how they impact communication and overall performance. Firstly, typical 1D and 2D distributions of the matrix involved in CG computations are considered. Then, a new 2D version of the CG method with asymmetric workload, based on leaving some threads idle during part of the computation to reduce communication, is proposed. The four strategies are independent of sparse storage schemes and are implemented using Unified Parallel C (UPC), a Partitioned Global Address Space (PGAS) language. The strategies are evaluated on two different platforms through a set of matrices that exhibit distinct sparse patterns, demonstrating that our asymmetric proposal outperforms the others except for one matrix on one platform.     

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain.     

Speeding up algorithm selection using average ranking and active testing by introducing runtime

Algorithm selection methods can be speeded-up substantially by incorporating multi-objective measures that give preference to algorithms that are both promising and fast to evaluate. In this paper, we introduce such a measure, A3R, and incorporate it into two algorithm selection techniques: average ranking and active testing. Average ranking combines algorithm rankings observed on prior datasets to identify the best algorithms for a new dataset. The aim of the second method is to iteratively select algorithms to be tested on the new dataset, learning from each new evaluation to intelligently select the next best candidate. We show how both methods can be upgraded to incorporate a multi-objective measure A3R that combines accuracy and runtime. It is necessary to establish the correct balance between accuracy and runtime, as otherwise time will be wasted by conducting less informative tests. The correct balance can be set by an appropriate parameter setting within function A3R that trades off accuracy and runtime. Our results demonstrate that the upgraded versions of Average Ranking and Active Testing lead to much better mean interval loss values than their accuracy-based counterparts.     

应用遗传算法构建化学模式分类器

神经网络和统计分析所构建的分类器均为复杂算式,难以体现专业知识;而分类规则直接以属性值为条件,确定个体类别,易于专业分析。对于连续属性的样本数据,本文应用基于信息熵的Chi-merge方法将其离散化,并将提取最优规则转换为组合优化问题,进而采用遗传算法求解。为此,本文将规则提取演绎为种群进化,并设计了个体适应度函数。由此提取出最优的分类规则,经过修剪处理后,与判别准则一起构成模式分类器。本文将其应用于橄榄油产地判别,所建立的分类器简单明了,规则数少,性能良好,适用于化学模式分类。     

Block adaptive shifting algorithm for filtering based on the Toeplitz preconditioned conjugate gradient technique

We present a new block adaptive algorithm as a variant of the Toeplitz-preconditioned block conjugate gradient (TBCG) algorithm. The proposed algorithm is formulated by combining TBCG algorithm with a data-reusing scheme that is realized by processing blocks of data in an overlapping manner, as in the optimum block adaptive shifting (OBAS) algorithm. Simulation results show that the proposed algorithm is superior to the block conjugate gradient shifting (BCGS), TBCG and Toeplitz-OBAS (TOBAS) algorithms in both convergence rate and tracking property of input signal conditioning.     

Research on the conjugate gradient algorithm with a modified incomplete Cholesky preconditioner on GPU

In this study, we discover the parallelism of the forward/backward substitutions (FBS) for two cases and thus propose an efficient preconditioned conjugate gradient algorithm with the modified incomplete Cholesky preconditioner on the GPU (GPUMICPCGA). For our proposed GPUMICPCGA, the following are distinct characteristics: (1) the vector operations are optimized by grouping several vector operations into single kernels, (2) a new kernel of inner product and a new kernel of the sparse matrix–vector multiplication with high optimization are presented, and (3) an efficient parallel implementation of FBS on the GPU (GPUFBS) for two cases are suggested. Numerical results show that our proposed kernels outperform the corresponding ones presented in CUBLAS or CUSPARSE, and GPUFBS is almost 3 times faster than the implementation of FBS using the CUSPARSE library. Furthermore, GPUMICPCGA has better behavior than its counterpart implemented by the CUBLAS and CUSPARSE libraries.     

Fuzzy lattice classifier and its application to bearing fault diagnosis

In this work, we present a novel classification scheme named fuzzy lattice classifier (FLC) based on the lattice framework and apply it to the bearing faults diagnosis problem. Different from the fuzzy lattice reasoning (FLR) model developed in literature, there is no need to tune any parameter and to compute the inclusion measure in the training procedure in our new FLC model. It can converge rapidly in a single pass through training patterns with a few induced rules. A series of experiments are conducted on five popular benchmark datasets and three bearing datasets to evaluate and compare the presented FLC with the FLR model as well as some other widely used classification methods. Experimental results indicate that the FLC yields a satisfactory classification performance with higher computation efficiency than other classifiers. It is very desirable to utilize the FLC scheme for on-line condition monitoring of bearings and other mechanical systems.     

Speeding up the testing and training time for the support vector machines with minimal effect on the performance

The Journal of Supercomputing - Support vector machine faces some problems associated with training time in the presence of large data sets due to the need for high memory and high computational...     

Fast evolving neuro-fuzzy model and its application in online classification and time series prediction

A strategy for improving speed of the previously proposed evolving neuro-fuzzy model (ENFM) is presented in this paper to make it more appropriate for online applications. By considering a recursive extension of Gath?CGeva clustering, the ENFM takes advantage of elliptical clusters for defining validity region of its neurons which leads to better modeling with less number of neurons. But this necessitates the computing of reverse and determinant of the covariance matrices which are time consuming in online applications with large number of input variables. In this paper a strategy for recursive estimation of singular value decomposition components of covariance matrices is proposed which converts the burdensome computations to calculating reverse and determinant of a diagonal matrix while keeping the advantages of elliptical clusters. The proposed method is applied to online detection of epileptic seizures in addition to prediction of Mackey?CGlass time series and modeling a time varying heat exchanger. Simulation results show that required time for training and test of fast ENFM is far less than its basic model. Moreover its modeling ability is similar to the ENFM which is superior to other online modeling approaches.     

基于SMO算法的SVM分类器在压缩机故障识别中应用

采用时频分析和支持向量机(SVM)相结合,提出一种压缩机故障识别新方法。首先利用Labview软件平台,对压缩机振动信号进行时频分析;然后提取出空气压缩机故障信号的特征向量,组成训练样本和测试样本;最后使用一对一方法构造成多元支持向量机分类器,利用序列最小优化(S M O)算法对故障样本进行训练,实现了压缩机的故障识别。实验测试表明,该分类器有较高故障诊断效率且性能良好,适合压缩机的故障识别。     

On the modified conjugate gradient method in cloth simulation

The seminal paper on cloth simulation by Baraff and Witkin [4] presents a modified preconditioned conjugate gradient (MPCG) algorithm for solving certain large, sparse systems of linear equations. These arise when employing implicit time integration methods aimed at achieving large step cloth simulation in the presence of constraints.This paper improves the robustness and efficiency of this MPCG algorithm. We prove convergence. For this, we recast the algorithm into a linear algebra setting, identifying its filtering procedure as an orthogonal projection. This leads not only to a convergence proof but also to a correction in the initiation stage of the original algorithm that improves its efficiency. We give an example to illustrate the performance improvement offered by this correction.