Composite of adaptive support vector regression and nonlinear conditional heteroscedasticity tuned by quantum minimization for forecasts

Adaptive support vector regression (ASVR) applied to the forecast of complex time series is superior to the other traditional prediction methods. However, the effect of volatility clustering occurred in time-series actually deteriorates ASVR prediction accuracy. Therefore, incorporating nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) model into ASVR is employed for dealing with the problem of volatility clustering to best fit the forecast’s system. Interestingly, quantum-based minimization algorithm is proposed in this study to tune the resulting coefficients between ASVR and NGARCH, in such a way that the ASVR/NGARCH composite model can achieve the best accuracy of prediction. Quantum optimization here tackles so-called NP-completeness problem and outperforms the real-coded genetic algorithm on the same problem because it accomplishes better approach to the optimal or near-optimal coefficient-found. It follows that the proposed method definitely obtains the satisfactory results because of highly balancing generalization and localization for composite model and thus improving forecast accuracy. Bao Rong Chang is currently an Associate Professor in the Department of Computer Science and Information Engineering at National Taitung University in Taitung, Taiwan. He completed his BS degree from the Department of Electronic Engineering, Tam Kang University, Taiwan. In 1990, he earned his ME degree from the Department of Electrical Engineering, University of Missouri-Columbia, USA, and his Ph.D. in 1994 at the same University. His current research interests include Intelligent Computations, Applied Computer Network, and Financial Engineering. Hsiu-Fen Tsai is currently a Senior Lecturer in the Department of International Business at Shu Te University in Kaohsiung, Taiwan. She completed her BA degree from the Department of International Business, National Taiwan University, Taiwan. In 1995, she earned her MBA degree from the Department of Business Administration, National Taiwan University, Taiwan. At present, she is a Ph. D. Candidate in Department of International Business since 2004 at the same University. Her current research interests include Intelligent Analysis of Business Models and Applications of Strategy Management.     

一种ε可调的在线支持向量回归及其训练算法

标准支持向量回归问题中,噪声较大的时段将包含较多的支持向量。提出一种时间窗内?着可调的支持向量回归方法,根据各时间窗的支持向量的比例动态调整?着,能够处理噪声时变的回归问题。并给出一种?着调整时的在线训练算法,避免重复求解凸规划问题。实例表明该方法的泛化能力和拟合精度较标准支持向量回归为优。     

Timing of resources exploration in the behavior of firm – Innovative approach and empirical simulation

We have insight into the importance of resource exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the resources point of view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an innovative approach using quantum minimization (QM) to tune a composite model comprising adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness-of-fit. Consequently, the preceding methods involved in this problem truly explain the timing of resources exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.     

改进的FS算法选取支持向量回归机参数及应用*

针对支持向量回归机SVR的拟合精度和泛化能力取决于相关参数的选取,提出了基于改进FS算法的SVR参数选择方法,并应用于交通流预测的研究。FS(free search)算法是一种新的进化计算方法,提出基于相对密集度的灾变策略改进FS算法的个体初始位置选择机制,以扩大搜索空间,提高全局搜索能力。对实测交通流量进行滚动预测仿真实验,结果表明该方法优化SVR参数是有效、可行的,与经验估计法和遗传算法相比,得到的SVR模型具有更好的泛化性能和预测精度。     

改进型支持向量回归预测模型的轧机轧制力预测

对轧机轧制力预测模型进行研究.使用人工鱼群优化算法对支持向量回归(SVR)参数选取进行最优的参数组合,将粒子群优化算法引入到常规人工鱼群算法中,并对其进行改进,提高了人工鱼群算法的性能.研究结果表明:Ekelund模型的轧制力计算结果误差较大,超过了10％,常规SVR预测模型的轧制力预测精度低于10％,而本文研究的改进SVR预测模型得到的轧制力误差低于5％,说明通过人工鱼群算法优化SVR算法模型的参数能够提高预测模型的预测精度,并且预测消耗时间在3种预测模型中是最短的.     

选矿过程精矿品位自适应在线支持向量预测方法

本文提出了一种基于支持向量回归的选矿过程精矿品位自适应在线预测方法,通过使用新的混合核函数和参数在线更新机制提高了精矿品位的预测精度.在分析经典核函数特性后,构造了一种混合核函数以兼顾模型的学习能力与泛化能力,同时为了提高预测方法对选矿生产动态过程的适应性,模型依据新工况样本对现有样本集统计特性的影响,引入了模型参数自适应调整机制,并采用在线迭代学习机制更新模型,提高了模型的计算速度.使用某选矿厂生产实际数据进行实验分析,结果表明本文方法比现有方法在计算时间和预测精度上都有明显优势,适合应用于动态变化的选矿生产过程.     

铁水硅含量的混沌粒子群支持向量机预报方法

提出一种基于混沌粒子群优化(CPSO)的支持向量回归机(SVR)参数优化算法, 并使用该算法建立高炉铁水硅含量预测模型(CPSO–SVR), 对某大型钢铁厂高炉铁水硅含量的实际采集数据进行预测, 结果表明基于混沌粒子群优化算法寻优的参数建立的铁水硅含量支持向量回归预测模型具有良好的预测效果. 与最小二乘支持向量回归机(LS–SVR)、使用粒子群优化算法训练的神经网络(PSO–NN)进行比较, CPSO–SVR模型对铁水硅含量进行预测时预测绝对误差小于0.03的样本数占总测试样本数的百分比达到90%以上, 预测效果明显优于PSO–NN, 且比LS–SVR稳定性更强, 可用于高炉铁水硅含量的实际预测, 表明混沌粒子群优化算法是选取SVR参数的有效方法.     

Nonlinear model predictive control with relevance vector regression and particle swarm optimization

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.     

基于支持向量回归机的区域物流需求预测模型及其应用*

为了提高区域物流需求预测的能力,从区域经济等影响因素指标与区域物流需求之间的内在关系的角度,应用基于结构风险最小化准则的支持向量回归机（SVR）方法, 建立“影响因素—区域物流需求” SVR预测模型来研究预测区域物流需求问题。在选择适当的参数和核函数的基础上,对上海市物流需求量进行预测,发现该方法能获得较小的训练相对误差和测试相对误差。     

Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems

Boiler combustion optimization is a key measure to improve the energy efficiency and reduce pollutants emissions of power units. However, time-variability of boiler combustion systems and lack of adaptive regression models pose great challenges for the application of the boiler combustion optimization technique. A recent approach to address these issues is to use the least squares support vector machine (LS-SVM), a computationally attractive machine learning technique with rather legible training processes and topologic structures, to model boiler combustion systems. In this paper, we propose an adaptive algorithm for the LS-SVM model, namely adaptive least squares support vector machine (ALS-SVM), with the aim of developing an adaptive boiler combustion model. The fundamental mechanism of the proposed algorithm is firstly introduced, followed by a detailed discussion on key functional components of the algorithm, including online updating of model parameters. A case study using a time-varying nonlinear function is then provided for model validation purposes, where model results illustrate that adaptive LS-SVM models can fit variable characteristics accurately after being updated with the ALS-SVM method. Based on the introduction to the proposed algorithm and the case study, a discussion is then delivered on the potential of applying the proposed ALS-SVM method in a boiler combustion optimization system, and a real-life fossil fuel power plant is taken as an instance to demonstrate its feasibility. Results show that the proposed adaptive model with the ALS-SVM method is able to track the time-varying characteristics of a boiler combustion system.     

A modified support vector regression: Integrated selection of training subset and model

In recent years, support vector regression (SVR) has become an emerging and popular forecasting technique in the field of machine learning. However, it is subjected to the model selection and learning complexity O(K * N³), especially for a massive data set (N is the size of training dataset, and K is the number of search). How to simultaneously reduce K and N can give us insight and inspiration on designing an effective and accurate selection algorithm. To this end, this paper tries to integrate the selection of training subset and model for SVR, and proposes a nested particle swarm optimization (NPSO) by inheriting the model selection of the existing training subset based SVR (TS-SVR). This nested algorithm is achieved by adaptively and periodically estimating the search region of the optimal parameter setting for TS-SVR. Complex SVR, involving large-scale training data, can be seen as extensions of TS-SVRs, yielding a nested sequence of TS-SVRs with increasing sample size. The uniform design idea is transplanted to the above modeling process, and the convergence for the proposed model is proofed. By using two artificial regression problems, Boston housing and electric load in New South Wales as empirical data, the proposed approach is compared with the standard ones, the APSO-OTS-SVR, and other existing approaches. Empirical results show that the proposed approach not only can select proper training subset and parameter, but also has better generalization performance and fewer processing time.     

Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes

Multi-grade processes have played an important role in the fine chemical and polymer industries. An integrated nonlinear soft sensor modeling method is proposed for online quality prediction of multi-grade processes. Several single least squares support vector regression (LSSVR) models are first built for each product grade. For online prediction of a new sample, a probabilistic analysis approach using the statistical property of steady-state grades is presented. The prediction can then be obtained using the corresponding LSSVR model if its probability of the special steady-state grade is large enough. Otherwise, the query sample is considered located in the transitional mode because it is not similar to any steady-state grade. In this situation, a just-in-time LSSVR (JLSSVR) model is constructed using the most similar samples around it. To improve the efficiency of searching for similar samples of JLSSVR, a strategy combined with the characteristics of multi-grade processes is proposed. Additionally, the similarity factor and similar samples of JLSSVR can be determined adaptively using a fast cross-validation strategy with low computational load. The superiority of the proposed soft sensor is first demonstrated through a simulation example. It is also compared with other soft sensors in terms of online prediction of melt index in an industrial plant in Taiwan.     

Simulation and prediction of hydrological processes based on firefly algorithm with deep learning and support vector for regression

ABSTRACT

Hydrological processes are hard to accurately simulate and predict because of various natural and human influences. In order to improve the simulation and prediction accuracy of the hydrological process, the firefly algorithm with deep learning (DLFA) was used in this study to optimise the parameters of support vector for regression (SVR) automatically, and a prediction model was established based on DLFA and SVR. The hydrological process of Huangfuchuan in Fugu County, Shanxi Province was taken as the research object to verify the performance of the prediction model, and the results were compared with those by the other six prediction models. The experimental results showed that the proposed prediction model achieved improved prediction performance compared with the other six models.     

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,     

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression (SVR) is an important step in SVR design, which strongly affects the performance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters . First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search. This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods.     

Influence and tuning of tunable screws for microwave filters using least squares support vector regression

This article presents an approach that can analyze the influence of tunable screws and perform a computer‐aided tuning for microwave filters. In the approach, a machine‐learning model that reveals the influence of tunable screws on the filter response is first developed by least squares support vector regression, according to some data from the tuning experience of filters. Then a computer‐aided tuning procedure based on the model is proposed, and the obtained adjusting amount of tunable screws can assist an unskilled operator to perform a fast and accurate tuning. The approach is validated by some experiments and the results confirm the effectiveness. The approach is particularly suitable to the computer‐aided tuning of volume‐producing filters. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.