GMDH网络在进出口贸易预测中的应用

随着世界经济一体化的加快,对外进出口贸易在一国经济中发挥越来越重要的作用。进出口贸易影响因素较多,波动性大,一般方法难以准确预测。针对该问题,采用基于PSO优化的GMDH网络预测出口贸易总额,该方法首先根据输入数据特点自适应构造GMDH网络,然后用PSO算法优化GMDH网络权值,最终得到较为精确的预测器。通过仿真结果表明,该方法能够较为准确的预测中国年度进出口贸易总额。     

Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons

In this paper, we propose and investigate a new category of neurofuzzy networks—fuzzy polynomial neural networks (FPNN) endowed with fuzzy set-based polynomial neurons (FSPNs) We develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms (GAs) in particular. The conventional FPNNs developed so far are based on the mechanisms of self-organization, fuzzy neurocomputing, and evolutionary optimization. The design of the network exploits the FSPNs as well as the extended group method of data handling (GMDH). Let us stress that in the previous development strategies some essential parameters of the networks (such as the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables) being available within the network are provided by the designer in advance and kept fixed throughout the overall development process. This restriction may hamper a possibility of developing an optimal architecture of the model. The design proposed in this study addresses this issue. The augmented and genetically developed FPNN (gFPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNNs. The GA-based design procedure being applied at each layer of the FPNN leads to the selection of the most suitable nodes (or FSPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation in which we use a number of modeling benchmarks—synthetic and experimental data being commonly used in fuzzy or neurofuzzy modeling. The obtained results demonstrate the superiority of the proposed networks over the models existing in the references.     

人工神经网络BP算法的改进及其应用

对传统的ＢＰ算法进行了改进,提出了ＢＰ神经网络动态全参数自调整学习算法,又将其编制成计算机程序,使得隐层节点和学习速率的选取全部动态实现,减少了人为因素的干预,改善了学习速率和网络的适应能力。计算结果表明：ＢＰ神经网络动态全参数自调整算法较传统的方法优越,训练后的网络模型不仅能准确地拟合训练值,而且能较精确地预测未来趋势。     

基于GMDH算法的配电网线损数据预处理研究

针对当前配电网线损计算的特点及数据存在缺失、异常等情况,基于数据分组处理算法(GMDH)建立了配电网线损缺失数据的预处理模型,实现对线损缺失数据的预处理。模型基于最邻近算法确定因变量和自变量缺失值的上下限,并进行随机插补,建立所有变量的数据分组处理模型,寻找最优复杂度模型,计算缺失值并进行迭代循环。算例结果表明,模型计算结果误差小、运算速度快,对缺失的线损数据能进行有效的动态更新,提升了数据质量,优化线损计算分析结果。     

改进GMDH与润滑油生产过程数学模型

在GMDH方法基础上,提出了一种改进方法：GMDH＋模型结构优化方法。应用改进方法对润滑油生产中酮苯脱蜡过程的数学模型进行了辨识工作,得出的模型与实际操作数据较为吻合。并根据得出的数学模型进行了优化计算,并寻找以收率最大为指标的最优操作点,结果较好。     

神经网络算法在股指预测中的应用

GMDH是一种具有自组织特征的数据处理方法，适用于非线性系统的建模，股指是一种重要的金融数据，具有混沌特性。该文将相空间重构引入了GMDH神经网络的建模中，并将之应用于道琼斯等股指的预测。同BP冲经网络方法及一阶局域预测法相比，GMDH获得了更好的预测效果。     

Modeling of the pervaporation process for isobutanol purification from aqueous solution using intelligent systems

In this study, intelligent systems (ANN-GA and GMDH) was employed to develope a model based on experimental data to predict the performance of the pervaporation process. The ANN system was coupled with the genetic algorithm (GA) to choose initial connection weights and biases of the multi-layer feed forward neural network (FFNN). The input parameters were the feed concentration, membrane thickness, and Reynolds number, while the outputs were total flux and permeate concentration. The RMSE of the estimated total flux for the ANN-GA and GMDH were 0.09170 and 0.0903, respectively. Also, the RMSE of estimated permeate concentration for the ANN-GA and GMDH were 0.0994 and 0.0975, respectively. The results indicated that the models had sufficient accuracy, but that GMDH could provide a better outcome. Finally, the relative importance of input variables on the network outputs was determined. Sensitive analysis showed that the membrane thickness and feed concentration are the most effect on the total flux and permeate concentration, respectively. Other variables also have important effect on the PV process and cannot be ignored.     

Presentation of regression analysis,GP and GMDH models to predict the pedestrian density in various urban facilities

In this study, the relationship between space mean speed (SMS), flow rate and density of pedestrians was investigated in different pedestrian facilities, including 1 walkway, 2 sidewalks, 2 signalized crosswalks and 2 mid-block crosswalks. First, statistical analysis was performed to investigate the normality of data and correlation of variables. Regression analysis was then applied to determine the relationship between SMS, flow rate, and density of pedestrians. Finally, two prediction models of density were obtained using genetic programming (GP) and group method of data handling (GMDH) models, and k-fold and holdout cross-validation methods were used to evaluate the models. By the use of regression analysis, the mathematical relationships between variables in all facilities were calculated and plotted, and the best relationships were observed in flow rate-density diagrams. Results also indicated that GP had a higher R² than GMDH in the prediction of pedestrian density in terms of flow rate and SMS, suggesting that GP was better able to model SMS and pedestrian density. Moreover, the application of k-fold cross-validation method in the models led to better performances compared to the holdout cross-validation method, which shows that the prediction models using k-fold were more reliable. Finally, density relationships in all facilities were obtained in terms of SMS and flow rate.     

Experimental study of the effect of HHO gas injection on pollutants produced by a diesel engine at idle speed

In this study, with the aim of reducing the energy consumption in the production of HHO gas for use in the combustion process of diesel fuel, different modes of gas production were investigated using electrolyzers. According to previous studies, the energy consumption rate of the electrolyzer to produce a high volumetric flow of HHO gas is very high. This high rate will restrict the use of equipment such as high-capacity batteries. The effects of HHO gas injection at the idle speed of the engine at a low temperature were evaluated. Because in this situation, the engine makes high air pollution. The results showed that the percentage of CO, CO2, HC, and NOX gases decreased by 66%, 33%, 38%, and 11%, respectively. On the other hand, the amount of O2 gas in the exhaust increased by 18%. These results were reported for HHO gas injection from 10 to 45 ml/s. The performance of Group Method of Data Handling (GMDH) neural network was desirable in modeling diesel engine pollutants. Because the Root-Mean-Square Error (RMSE) criterion for all evaluated gases is less than 0.32. The GMDH neural network was used for modeling the operation of the diesel engine with HHO supplemental fuel. The GMDH results showed that this artificial network can measure all engine exhaust gases. It can be used as a sensor and virtual simulator for this diesel engine with HHO supplemental fuel.     

具有混沌特征的GMDH网络在降雨量预测中的应用

ＧＭＤＨ称为数据处理的群集方法,它的网络结构有自组织特征,适用于非线性系统的建模。降雨量是一种重要的灾害数据,具有混沌特性,本文将降雨量的混沌特征引入神经网络ＧＭＤＨ的建模,并对安徽省蚌埠地区的降雨量进行了预测,收到良好效果。