基于ARMA-GMDH的水利工程变形预测模型

变形监测数据是定量评价水利工程结构安全的重要依据。水利工程变形数据是一种典型的非平稳信号,同时包含线性成分与非线性成分。针对水利工程变形的线性成分和非线性成分特征,分别利用针对线性信号的自回归移动平均模型和非线性信号的数据分组处理方法,构建了一种基于ARMA-GMDH的组合预测模型对水利工程的变形进行预测。工程实例表明,该方法可以有效地对水利工程变形的线性及非线性成分进行预测,与多个预测方法结果进行对比发现所提出的组合模型具有较高的预测精度,且与实测数据具有相似的变形趋势,可以分别对变形的线性及非线性成分规律进行分析,综合判断结构的变形趋势和安全性态,因此具有一定的工程应用价值。     

基于GMDH的卷烟工艺参数-指标关系模型研究

通过对烟草加工中工艺参数与质量指标之间的关系研究,提出采用自组织数据挖掘方法建立相应的关系模型,并利用该模型预测质量指标取值。通过与多元线性回归模型的预测值对比,证明了该方法的有效性。     

MULTI-OBJECTIVE OPTIMIZATION OF ABRASIVE FLOW MACHINING PROCESSES USING POLYNOMIAL NEURAL NETWORKS AND GENETIC ALGORITHMS

Abrasive flow machining (AFM) is an economic and effective non-traditional machining technique, which is capable of providing excellent surface finish on difficult to approach regions on a wide range of components. With this method, it has become possible to substitute various time-consuming deburring and polishing operations that had often lead to non-reproducible results. In this paper, group method of data handling (GMDH)-type neural networks and Genetic algorithms (GAs) are first used for modelling of the effects of number of cycles and abrasive concentration on both material removal and surface finish, using some experimentally obtained training and testing data for brass and aluminum. Using such polynomial neural network models obtained, multi-objective GAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are then used for Pareto-based optimization of AFM considering two conflicting objectives such as material removal and surface finish. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of AFM can be discovered by the Pareto-based multi-objective optimization of the obtained polynomial models. Such important optimal principles would not have been obtained without the use of both GMDH-type neural network modelling and multi-objective Pareto optimization approach.     

A dynamic classifier ensemble selection approach for noise data

Dynamic classifier ensemble selection (DCES) plays a strategic role in the field of multiple classifier systems. The real data to be classified often include a large amount of noise, so it is important to study the noise-immunity ability of various DCES strategies. This paper introduces a group method of data handling (GMDH) to DCES, and proposes a novel dynamic classifier ensemble selection strategy GDES-AD. It considers both accuracy and diversity in the process of ensemble selection. We experimentally test GDES-AD and six other ensemble strategies over 30 UCI data sets in three cases: the data sets do not include artificial noise, include class noise, and include attribute noise. Statistical analysis results show that GDES-AD has stronger noise-immunity ability than other strategies. In addition, we find out that Random Subspace is more suitable for GDES-AD compared with Bagging. Further, the bias-variance decomposition experiments for the classification errors of various strategies show that the stronger noise-immunity ability of GDES-AD is mainly due to the fact that it can reduce the bias in classification error better.     

MULTI-OBJECTIVE OPTIMIZATION OF ABRASIVE FLOW MACHINING PROCESSES USING POLYNOMIAL NEURAL NETWORKS AND GENETIC ALGORITHMS

Abrasive flow machining (AFM) is an economic and effective non-traditional machining technique, which is capable of providing excellent surface finish on difficult to approach regions on a wide range of components. With this method, it has become possible to substitute various time-consuming deburring and polishing operations that had often lead to non-reproducible results. In this paper, group method of data handling (GMDH)-type neural networks and Genetic algorithms (GAs) are first used for modelling of the effects of number of cycles and abrasive concentration on both material removal and surface finish, using some experimentally obtained training and testing data for brass and aluminum. Using such polynomial neural network models obtained, multi-objective GAs (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are then used for Pareto-based optimization of AFM considering two conflicting objectives such as material removal and surface finish. It is shown that some interesting and important relationships as useful optimal design principles involved in the performance of AFM can be discovered by the Pareto-based multi-objective optimization of the obtained polynomial models. Such important optimal principles would not have been obtained without the use of both GMDH-type neural network modelling and multi-objective Pareto optimization approach.     

Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Financial prediction has attracted a lot of interest due to the financial implications that the accurate prediction of financial markets can have. A variety of data driven modelling approaches have been applied but their performance has produced mixed results. In this study we apply both parametric (neural networks with active neurons) and nonparametric (analog complexing) self-organising modelling methods for the daily prediction of the exchange rate market. We also propose a combined approach where the parametric and nonparametric self-organising methods are combined sequentially, exploiting the advantages of the individual methods with the aim of improving their performance. The combined method is found to produce promising results and to outperform the individual methods when tested with two exchange rates: the American Dollar and the Deutche Mark against the British Pound.     

基于GMDH的中长期电力负荷组合预测模型

组合负荷预测模型能够充分利用数据信息,有效降低预测风险、改善预测效果,在中长期负荷预测中获得了广泛应用。而目前的组合预测模型实质大都为单一预测模型的加权平均,没有能够充分发挥综合预测的优势.应用数据分组处理方法（GMDH）进行组合预测,在充分考虑各单一模型特点和预测效果的基础上,形成多元非线性组合预测模型,自动从数据中挖掘出重要信息,克服了传统组合预测模型建模中的主观因素影响,可以改善预测精度。并将该预测模型应用于实际电网,计算结果表明该模型有效提高了预测精度,适用于中长期负荷预测.     

Two-level Multi-surrogate Assisted Optimization method for high dimensional nonlinear problems

Curse of dimensionality is a key issue in engineering optimization. When the dimension increases, distribution of samples becomes sparse due to expanded design space. To obtain accurate and reliable results, the amount of samples often grows exponentially with the dimensions. To improve the efficiency of the surrogate with limited samples, a Two-level Multi-surrogate Assisted Optimization (TMAO) is suggested. The framework of the TMAO is to decompose a complicated problem into separable and non-separable components. In the first-level, High Dimensional Model Representation (HDMR) is utilized to determine the correlations among input variables. Then, a high dimensional problem can be decomposed into separable and non-separable components. Thus, the dimension of the original problem might be reduced significantly. Moreover, considering noises and outliers, Support Vector Regression (SVR)-HDMR is utilized to obtain more reliable surrogate. Expected Improvement (EI) criterion is suggested to generate new samples to save computational cost. In the second-level, to handle the non-separable component, a multi-surrogate assisted sampling strategy is suggested. Compared with other methods, the distinctive characteristic of the suggested sampling strategy is to use different surrogates to search potential uncertain regions. Considering the diversity of surrogates, more feature samples might be generated close to the local optimum. Even though it is still difficult to find a global solution, it could help us to find a feasible solution in practice. To verify the performance of the suggested method, several high dimensional mathematical functions are tested by the suggested method. The results demonstrate that all test functions can be successfully solved.     

基于改进GMDH算法的路口短时交通流量预测

城市交通是一个复杂的大系统,实时而准确的短时交通流量预测,可以为城市交通诱导和控制提供科学支持。针对GMDH算法建模泛化能力差的问题,结合集成学习的思想对GMDH算法进行改进,并将改进的算法应用到短时交通流量模型的构建中。结果表明,该方法可以有效地对短时交通流量进行预测,建模平均相对误差为1.10%,预测相对误差为0.58%。     

基于两水平算法的入侵检测系统的研究

针对现有入侵检测系统的检测时间范围具有一定局限性的缺陷,提出了一种基于两水平算法的入侵检测系统模型(TAIDS)。该模型利用两水平算法结合GMDH多层迭代的求解方法,按照不同时间范围的数据同时建模,可扩大系统检测入侵行为的时间范围。通过目标系统分析,寻求入侵影响因素之间的关系,建立最优模型, 有效地降低了漏检率和误检率。给出了系统框架和建模算法具体步骤,通过与Snort和NIDES入侵检测系统的仿真实验比较,证明该系统模型的有效性。