Predictive modeling of mercury speciation in combustion flue gases using GMDH-based abductive networks

Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First-principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modern data-based machine learning techniques has been recently introduced, including neural networks. Here we propose an alternative approach using abductive networks based on the group method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model synthesis, automatic selection of significant inputs, and more transparent input–output model relationships. Models were developed for predicting three types of mercury speciation (elemental, oxidized, and particulate) using a small dataset containing six inputs parameters on the composition of the coal used and boiler operating conditions. Prediction performance compares favourably with neural network models developed using the same dataset, with correlation coefficients as high as 0.97 for training data. Network committees (ensembles) are proposed as a means of improving prediction accuracy, and suggestions are made for future work to further improve performance.     

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.     

改进的GMDH方法及其在参数预报中的应用

本文在综合了ＧＭＤＨ方法中几种有效的改进算法的基础上，进一步研究了变量预选及部分表达式的构成问题，采用最优化方法确定变量最佳组成式并进行变量筛选，由此得到精度更高、稳定性较好的模型，将其应用于聚丙烯熔指数的预报，仿真结果令人满意。     

Structure identification of Bayesian classifiers based on GMDH

This paper introduces group method of data handing (GMDH) theory to Bayesian classification, and proposes GMBC algorithm for structure identification of Bayesian classifiers. The algorithm combines two structure identification ideas of search & scoring and dependence analysis, and is able to accomplish the process of adaptive structure identification. We experimentally test two versions of Bayesian classifiers (GMBC-BDe and GMBC-BIC) over 25 data sets. The results show that, the structure identification of the two Bayesian classifiers especially GMBC-BDe is very effective. And when the data sets contain lots of noise, the superiority of Bayesian classifiers learned by GMBC is more obvious. Finally, giving a classification domain without any prior information about the noise, we recommend adopting GMBC-BDe rather than GMBC-BIC.     

Global Optimization Based on Heuristic Self-Organization: The Discrete Case

Application of the heuristic self-organization method (or the so-called Group Method of Data Handling) to the solution of discrete optimization problems is considered. Convergence by probability of the self-organization process to a global optimization point is proved. Some examples of numerical experiments are presented.     

基于ＨＦＬＡＮＮ自组织多项式网络学习算法

首先提出一种双曲函数型神经网络HFLANN,设计出一类基于HFLANN网络的层次双曲型函数网络HHFLANN,给出了HHFLANN的网络学习算法,使其在用于非线性的拟合中体现了较强的优越性,对于任意的Volterra级数使用HHFLANN网络来逼近是完全可行的,该算法较GMDH算法和SOP算法,具有快速简单的特性,它优于GMDH算法,有规律地选取部分多项式;优于SOP算法,在构造SOP网络不需要太多的中间隐层,从而加快了学习过程,提高了网络的逼近性能,更适合于具有层次结构的应用领域。     

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。     

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。     

地理信息系统中地图无级漫游的实现方法之研究

本文在分析现存GIS软件中处理多幅不同比例尺地图的缩放漫游时的不足的基础上,分别从横向、纵向两个方面提出了多幅不同比例尺地图的无级漫游方法,并对方法的实现进行了探讨。该方法已经成功应用于国家“十五”科技攻关项目“面向行政区域的国民经济与社会发展的辅助决策支持技术的应用”中,取得了良好的应用效果。     

A Precision Audio-Frequency Voltage Regulator

SUMMARY

A circuit is described which, when fed with an alternating voltage whose amplitude may vary, produces an accurately settable, continuously variable output amplitude. The error in the output amplitude is within 0.1% for inputs from 3 V to 7 V rms, between 30 Hz and 4 kHz, and within 1% between 16 Hz and 10 kHz. By connection to this inexpensive circuit, any simple, inexpensive audio oscillator is effectively converted into a multirange, precision-amplitude ac source.