聚类优化RBF网络在锅炉燃烧中的应用

为了提高径向神经网络的训练精度,提出一种混合优化算法.算法将基于萤火虫算法的模糊聚类,应用到径向神经网络基函数中心向量的计算中,利用萤火虫算法良好的全局寻优能力来优化搜索基函数中心,提高了获取网络类中心的稳定性.锅炉燃烧优化的实例表明,混合优化算法达到了预期效果,提升了锅炉燃烧效率.     

一种基于无监督学习的社交网络流量快速识别方法

在分析了社交网络的发展和研究现状后,结合现有的网络流量识别方法和社交网络流量特征属性,提出了一种基于KMeans聚类算法的无监督学习社交网络流量识别方法.为了提高处理的高效性、实时性,利用开源云计算平台如doop上提供的M印Reduce架构进行分布式并行处理.对比实验结果表明,提出的方法能快速、高效的识别社交网络流量,并且识别准确率有显著提高.     

混沌粒子群神经网络在上证指数预测中的应用

上证指数预测是一个非常复杂的非线性问题,为了提高对上证指数预测的准确性,本文采用基于混沌粒子群(CPSO)算法对BP神经网络算法改进的方法来进行预测.BP神经网络算法目前已经应用到预测、聚类、分类等许多领域,取得了不少的成果.但自身也有明显的缺点,比如易陷入局部极小值、收敛速度慢等.用混沌粒子群算法改进BP神经网络算法的基本思想是用混沌粒子群算法优化BP神经网络算法的权值和阈值,在粒子群算法中加入混沌元素,提高粒子群算法的全局搜索能力.对上证指数预测的结果表明改进后的预测方法,具有更好的准确性.     

基于矩阵变换的聚类集成优化模型

聚类集成方法能够有效综合不同的聚类结果,提高聚类的精确度和稳定性.提出了一个基于矩阵变换的聚类集成优化模型,模型通过矩阵变换代替传统方法中的聚类配准模式,使得优化模型更加简洁,然后给出了求解该优化模型的叠代算法.实验表明,提出的聚类集成方法能够有效提高聚类集成的稳定性和精确度,并且在聚类数目比较少时,算法有着较低的时间复杂度.     

Extracting interpretable fuzzy models for nonlinear systems using gradient-based continuous ant colony optimization

This paper exploits the ability of a novel ant colony optimization algorithm called gradient-based continuous ant colony optimization, an evolutionary methodology, to extract interpretable first-order fuzzy Sugeno models for nonlinear system identification. The proposed method considers all objectives of system identification task, namely accuracy, interpretability, compactness and validity conditions. First, an initial structure of model is obtained by means of subtractive clustering. Then, an iterative two-step algorithm is employed to produce a simplified fuzzy model in terms of number of fuzzy sets and rules. In the first step, the parameters of the model are adjusted by utilizing the gradient-based continuous ant colony optimization. In the second step, the similar membership functions of an obtained model merge. The results obtained on three case studies illustrate the applicability of the proposed method to extract accurate and interpretable fuzzy models for nonlinear system identification.     

混合优化RBF-BP网络的板形缺陷识别

针对传统板形模式识别方法存在精度低、鲁棒性弱的问题,提出了一种混合优化RBF-BP组合神经网络板形模式识别方法。首先利用自组织映射网络(SOM)对样本聚类,利用聚类后的网络拓扑结构确定RBF的中心,并计算RBF的宽度,克服了传统聚类算法随机选取中心导致聚类结果不稳定的问题。然后利用遗传算法(GA)良好的全局搜索能力优化整个网络的权值。RBF-BP组合神经网络是由一个RBF子网和一BP子网串联构成的,该网络同时具备BP神经网络能较好地预测未知样本的能力以及RBF神经网络的逼近速度快的优点。并以某900HC可逆冷轧机板形识别为应用背景,在MATLAB2010a环境下进行仿真实验,结果表明混合优化RBF-BP组合神经网络的板形模式识别方法能够识别出常见的板形缺陷,提高了板形缺陷识别精度并具有较好的鲁棒性,可以满足板带轧机高精度的板形控制要求。     

XGobi: Interactive Dynamic Data Visualization in the X Window System

Abstract

XGobi is a data visualization system with state-of-the-art interactive and dynamic methods for the manipulation of views of data. It implements 2-D displays of projections of points and lines in high-dimensional spaces, as well as parallel coordinate displays and textual views thereof. Projection tools include dotplots of single variables, plots of pairs of variables, 3-D data rotations, various grand tours, and interactive projection pursuit. Views of the data can be reshaped. Points can be labeled and brushed with glyphs and colors. Lines can be edited and colored. Several XGobi processes can be run simultaneously and linked for labeling, brushing, and sharing of projections. Missing data are accommodated and their patterns can be examined; multiple imputations can be given to XGobi for rapid visual diagnostics. XGobi includes an extensive online help facility. XGobi can be integrated in other software systems, as has been done for the data analysis language S, the geographic information system (GIS) Arc View?, and the interactive multidimensional scaling program XGvis. XGobi is implemented in the X Window System? for portability as well as the ability to run across a network.     

A sequential method for the development of visual interactive meta-simulation models using neural networks

This paper proposes a practical and efficient method for the development of visual interactive meta-simulation models using neural networks. The method first uses a randomised simulation experimental design to obtain a set of results from a previously validated simulation model. The bootstrap technique is used on these results to generate a series of neural network models that are then trained using back propagation. The visual interactive meta-simulation model consists of the collective response from the trained neural network models. The accuracy of the meta-simulation model is assessed using the bootstrap technique and improved accuracy obtained by increasing the size of the randomised simulation experimental design set and re-training. This paper describes the approach, gives results for five example problems and suggests that the method is a practical extension to visual interactive simulation.     

An improved clustering method based on biological visual models

A clustering methodology based on biological visual models that imitates how humans visually cluster data by spatially associating patterns has been recently proposed. The method is based on Cellular Neural Networks and some resolution adjustments. The Cellular Neural Network rebuilds low-density areas while different resolutions find the best clustering option. The algorithm has demonstrated good performance compared to other clustering techniques. However, its main drawbacks correspond to its inability to operate with more than two-dimensional data sets and the computational time required for the resolution adjustment mechanism. This paper proposes a new version of this clustering methodology to solve such flaws. In the new approach, a pre-processing stage is incorporated featuring a Self-Organization Map that maps complex high-dimensional relations into a reduced lattice yet preserving the topological organization of the initial data set. This reduced representation is employed as the two-dimensional data set for further processing. In the new version, the resolution adjustment process is also accelerated through the use of an optimization method that combines the Hill-Climbing and the Random Search techniques. By incorporating such mechanisms rather than evaluating all possible resolutions, the optimization strategy finds the best resolution for a clustering problem by using a limited number of iterations. The proposed approach has been evaluated, considering several two-dimensional and high-dimensional datasets. Experimental evidence exhibits that the proposed algorithm performs the clustering task over complex problems delivering a 46% faster on average than the original method. The approach is also compared to other popular clustering techniques reported in the literature. Computational experiments demonstrate competitive results in comparison to other algorithms in terms of accuracy and robustness.     

Maximum likelihood least squares identification for systems with autoregressive moving average noise

Maximum likelihood methods are important for system modeling and parameter estimation. This paper derives a recursive maximum likelihood least squares identification algorithm for systems with autoregressive moving average noises, based on the maximum likelihood principle. In this derivation, we prove that the maximum of the likelihood function is equivalent to minimizing the least squares cost function. The proposed algorithm is different from the corresponding generalized extended least squares algorithm. The simulation test shows that the proposed algorithm has a higher estimation accuracy than the recursive generalized extended least squares algorithm.     

基于邻域粗糙集的网络入侵分类诊断组合模型研究

网络入侵诊断直接影响网络正常运行和安全.针对入侵类型复杂,现有分类诊断模型精度有限的问题,提出一种基于邻域粗糙集的网络入侵分类诊断优化模型.首先,运用邻域粗糙集对网络入侵数据进行条件属性的约简,确定关键属性,然后将其作为训练输入构建相关向量机分类诊断模型,并同时运用遗传算法进行超参数优化,提高模型诊断精度和速度.通过KDDCup99数据集对优化模型性能进行检验,结果表明,组合预测方法精确度高于支持向量机、相关向量机和BP神经网络.组合模型诊断精度高、速度快,具有优异的综合性能.     

Optimal load sharing in soft real-time systems using likelihood ratios

We consider a load-sharing problem for a multiprocessor system in which jobs have real-time constraints: if the waiting time of a job exceeds a given random amount (called the laxity of the job), then the job is considered lost. To minimize the steady-state probability of loss with respect to the load-sharing parameters, we propose to use the likelihood ratio derivative estimate approach, which has recently been studied for sensitivity analysis of stochastic systems. We formulate a recursive stochastic optimization algorithm using likelihood ratio estimates to solve the optimization problem and provide a proof for almost sure convergence of the algorithm. The algorithm can be used for on-line optimization of the real-time system and does not require a priori knowledge of the arrival rate of customers to the system or the service time and laxity distributions. To illustrate our results, we provide simulation examples.This research was partially supported by an IBM Graduate Fellowship and by the National Science Foundation through Grant No. ECS-87-15217.     

A fuzzy clustering approach to real-time demand-responsive bus dispatching control

Quick response (QR) to passenger needs is a key objective for advanced public transportation systems (APTS), and it has become increasingly important for contemporary metropolitan bus operations to gain a competitive advantage over private transportation. This paper presents a real-time control methodology for demand-responsive bus operations that respond quickly to passenger needs. The proposed method primarily involves two levels of functionality: (1) short-term forecasting of passenger demands using time-series prediction models, and (2) identification of service strategies coupled with the associated bus service segments using fuzzy clustering technologies in response to variances in passenger demand attributes and traffic conditions. The proposed bus operations method identifies the demand-responsive vehicle service strategies primarily according to the predicted up-to-date attributes of passengers’ demands, rather than deterministic passenger arrival rates, which were generally used in previous literature. In addition, the variation of traffic conditions along bus lines is considered in the proposed method. Results from numerical studies using real data of passengers’ demands, including passenger volume at each bus stop and the passenger origin-destination (O-D) patterns, are presented to demonstrate the effectiveness of the proposed method for real-world applications.     

Approximation of an Analog Diffusion Network with Applications to Image Estimation

This work is concerned with a numerical procedure for approximating an analog diffusion network. The key idea is to take advantage of the separable feature of the noise for the diffusion machine and use a parallel processing method to develop recursive algorithms. The asymptotic properties are studied. The main result of this paper is to establish the convergence of a continuous-time interpolation of the discrete-time algorithm to that of the analog diffusion network via weak convergence methods. The parallel processing feature of the network makes it attractive for solving large-scale optimization problems. Applications to image estimation are considered. Not only is this algorithm useful for the image estimation problems, but it is widely applicable to many related optimization problems.     

智能网联下无人车配送路径优化

结合智能网联无人车实时信息共享与路径选择的特点,研究其配送路径优化问题。通过引进关键点更新策略,制定路径预规划阶段和路径实时调整阶段无人车路径选择策略,提出智能网联环境下基于实时交通信息的车辆路径问题两阶段模型。其中,路径预规划阶段模型确定初始路径与每辆车服务的客户点,路径实时调整阶段模型对每辆车的路径实时调整。对于该优化模型设计遗传算法进行求解,并通过算例验证了模型与算法的可行性。研究结果表明,本文构建的无人车配送优化模型,有效的结合了无人车实时通信与路径选择的特点,节省了无人车配送时间。研究对于无人车在第三方物流配送领域的推广应用具有一定的探索意义。     

热传导(对流-扩散)方程源项识别的粒子群优化算法

提出了利用粒子群优化(PSO)算法反演热传导方程与对流-扩散方程源项的一种新方法,在已有文献方法的基础上,求解出这两类方程正问题的解析解,再把源项识别问题转化为最优化问题,结合粒子群优化算法寻优求解.通过数值模拟与统计检验,结果表明,此方法可快速有效地实现热传导方程与对流-扩散方程源项的识别,并可推广应用到其它数学物理方程的源项或参数的反演识别.     

A receding horizon D-optimization approach for model identification–oriented input design and application in combustion engines

In this paper, a receding horizon D-optimization approach for model identification–oriented input design is proposed, and a practical application is demonstrated for internal combustion engines. The proposed approach consists of a recursive parameter identification algorithm and an input signal design algorithm; where the latter provides D-optimal excitation signal for the adaptation of the parameter estimation in the following identification phase. The D-optimization algorithm is constructed with the Continuation/GMRES method, which provides an approximate solution according to the current parameter of the model. To validate effectiveness and feasibility of the proposed approach, testing results applying the proposed approach to an internal combustion engine are demonstrated and conducted on a full-scale engine test bench.     

正态云模型果蝇优化算法及其应用

提出了一种基于正态云模型的果蝇优化算法(NCMFOA).该算法通过直接将果蝇位置赋值给气味浓度判定值和引入正态云模型来刻画果蝇嗅觉搜索行为的随机性与模糊性,从而解决了果蝇优化算法(FOA)不能搜索负值空间的缺陷,并有效克服了FOA算法在解决复杂优化问题时容易陷入局部极值的不足.通过正态云模型熵值的动态调整,使得NCMFOA算法在进化的前期阶段具有较强的随机性与模糊性,以提高算法的全局探索能力;随着迭代次数的增加,算法搜索行为的随机性与模糊性逐渐减弱,使得其局部开发能力逐渐增强,算法收敛精度得到提高.此外,通过引入视觉实时更新方案,进一步加速了算法的收敛速度.用经典的基准测试函数验证了NCMFOA算法的可行性与有效性,结果表明该算法具有收敛速度快、收敛精度高以及鲁棒性好等优点,对于高维复杂优化问题,该算法同样获得了良好的优化效果.将NCMFOA算法用于解决混沌系统的参数估计问题,进一步验证了该算法具有较强的解决实际工程优化问题的能力.