New Indices for Characterizing Spatial Models of Ore Deposits by the Use of a Sensitivity Vector and an Influence Factor

Precise spatial estimation of ore grades and impurity contents from sample data limited in amount and location is indispensable to metallic and nonmetallic resource exploration. One of the advantages of using geostatistics for this purpose is that it can incorporate multivariate data into spatial estimation of one variable. However, there are two weak points concerning technical and post-processing problems. First is the difficulty in application to geologic data in which spatial correlations are not clear because of intrinsic nonlinear behavior. Second is the absence of indices to interpret the mechanisms and factors which govern the spatial distribution. To address these problems, a spatial method of modeling based on a feedforward neural network, SLANS, which recognizes the relationship between the data value and location by considering supplementary attributes such as lithology and biostratigraphy, and a sensitivity analysis using this network were developed. These methods were applied to two case studies, genetic mechanisms of kuroko deposits and quality assessment of a limestone mine. The first case study is a spatial analysis of principal metals of kuroko deposits (volcanogenic massive sulfide deposits) in the Hokuroku district, northern Japan. It was clarified that upward and downward sensitivity vectors were distinguished near the deposits inside and outside the tectonic basin, respectively. Sensitivity analysis for the second case study showed a strong effect of crystalline limestone on the important impurity, P₂O₅ contents. Hydrothermal alteration, which could cause leaching and secondary concentration of phosphorus, is considered to have produced this effect.     

BP网络学习参数模糊自适应算法的实现

前馈神经网络BP算法的改进方案中,对网络训练(学习)过程中学习率和惯性系数进行模糊自适应调节,以提高收敛速度,是一项很有效的措施。文中具体分析了如何根据设计者的先验知识确定模糊规则和隶属函数,并以三比特异或函数(或称奇偶分类)的实现为例,验证了这种算法的改进、加速了BP网络的学习过程。     

正弦干扰下时滞系统的状态预测观测器与前馈-反馈预测控制器设计

研究控制变量含有时滞的线性系统在外部正弦干扰下的最优减振预测控制问题。利用系统的控制向量和被控对象的预测输出向量 ,设计了 1种全维状态预测观测器。并将该状态观测器用于时滞控制系统的最优前馈 -反馈预测控制中。频域分析表明 ,应用该状态预测观测器可将闭环系统的时滞项移至系统闭环结构之外 ,从而其优化控制规律完全可以按无时滞系统进行设计。时域分析表明 ,设计的预测控制器对外部正弦干扰有较强的鲁棒性 ,得到的结果关于二次型平均性能指标是次优的     

RPROP算法在火成岩岩石分类中的应用

为了更好地解决火成岩岩石分类问题,引入一种快速实用的BP算法—Resilient Backpropagation(RPROP)算法.在说明RPROP算法的基础上,结合火成岩岩石资料,建立基于RPROP算法的BP网络火成岩岩石分类模型,进行火成岩岩石分类的应用研究.结果表明,应用RPROP算法进行火成岩岩石分类,分类的准确率高,与基本BP算法及其一些改进算法相比,训练速度快,具有很好的应用前景.     

用于成像光谱数据特征矿物识别的人工神经网络结构研究

当某一问题很难甚至无法用数学方法建立精确模型时,人工神经网络的方法则显示了优势。对于一个具体问题,采用何种网络结构是至关重要的。本文以美国内华达州Cuprite矿区成像光谱数据特征矿物识别为例,采用6种不同结构的多层前馈网络模型,从其训练难度、运算效率及识别效果等方面进行了综合对比分析。     

A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L'Écoulement Fluvial

Abstract

Artificial neural network (ANN) models provide huge potential for simulating nonlinear behaviour of hydrological systems. However, the potential of ANN is yet to be fully exploited due to the problems associated with improving the model generalization performance. Generalization refers to the ability of a neural network to correctly process input data that have not been used for calibrating the neural network model. In the hydrological context, better generalization performance implies higher precision of forecasting. The primary objectives of this study are to explore new measures for improving the generalization performance of an ANN-based rainfall–runoff model, and to evaluate the applicability of the new measures. A modified neural network model (entitled goal programming (GP) neural network) for modelling the rainfall–runoff process has been developed, in which three enhancements are made as compared to the widely-used backpropagation (BP) network. The three enhancements are (a) explicit integration of hydrological prior knowledge into the neural network learning; (b) incorporation of a modified training objective function; and (c) reduction of network sensitivity to input errors. Seven watersheds across a range of climatic conditions and watershed areas in China were selected for examining the alternative networks. The results demonstrate that the GP consistently outperformed the BP both in the calibration and verification periods and three proposed measures yielded improvement of performance.     

Improved dynamic testing by impedance control

In this paper, the issue of actuator-structure interaction in dynamic testing of structures is considered. The problem is approached from the novel standpoint of impedance control. It is shown that an effective strategy to design controls for dynamic testing is by designing the test system impedance. It is also shown that this can be achieved using feedforward compensation. The analysis is carried out in the context of displacement controlled dynamic testing, when the tested structure has a high and nonlinear stiffness. It is demonstrated that stable and accurate dynamic testing can be achieved using the proposed strategy, when this is not possible using traditional feedback control techniques. Furthermore, the impedance control and feedforward strategies are applied in the context of hybrid simulation, a technique of coupling computational and physical substructures applied in earthquake engineering. Here, a delay compensation scheme is necessary in addition to feedforward. Experimental results are presented that demonstrate both improved dynamic testing performance when impedance control is employed, and its applicability in hybrid simulation.     

Characterizing Content Distributions of Impurities in a Limestone Mine Using a Feedforward Neural Network

The factors determining the suitability of limestone for industrial use and its commercial value are the amounts of calcium oxide (CaO) and impurities. From 244 sample points in 18 drillhole sites in a limestone mine, southwestern Japan, data on four impurity elements, SiO₂, Fe₂O₃, MnO, and P₂O₅ were collected. It generally is difficult to estimate spatial distributions of these contents, because most of the limestone bodies in Japan are located in the accretionary complex lithologies of Paleozoic and Mesozoic age. Because the spatial correlations of content data are not clearly shown by variogram analysis, a feedforward neural network was applied to estimate the content distributions. The network structure consists of three layers: input, middle, and output. The input layer has 17 neurons and the output layer four. Three neurons in the input layer correspond with x, y, z coordinates of a sample point and the others are rock types such as crystalline and conglomeratic limestones, and fossil types related to the geologic age of the limestone. Four neurons in the output layer correspond to the amounts of SiO₂, Fe₂O₃, MnO, and P₂O₅. Numbers of neurons in the middle layer and training data differ with each estimation point to avoid the overfitting of the network. We could detect several important characteristics of the three-dimensional content distributions through the network such as a continuity of low content zones of SiO₂ along a Lower Permian fossil zone trending NE-SW, and low-quality zones located in depths shallower than 50 m. The capability of the neural network-based method compared with the geostatistical method is demonstrated from the viewpoints of estimation errors and spatial characteristics of multivariate data. To evaluate the uncertainty of estimates, a method that draws several outputs by changing coordinates slightly from the target point and inputting them to the same trained network is proposed. Uncertainty differs with impurity elements, and is not based on just the spatial arrangement of data points.     

多种群并行进化神经网络的研究及应用

提出一种新的多种群并行遗传算法 (NMPGA) ,并将其作为多层前馈神经网络(MFNNs)的学习算法 ,从而形成一类新的 MFNN模型——多种群并行进化神经网络(MPENNs)。首先 ,对一给定的网络结构 ,随机产生一初始权重的集合 ,这个集合实际上对应着一组具有相同结构但不同权重的神经网络。然后 ,采用 NMPGA对 MFNNs的权重进行进化。最后 ,性能最好的网络被选作目标问题的解。在 NMPGA算法中 ,作者采用浮点数编码来克服传统二进制编码的精度不足问题 ,并设计了专门的杂交算子和变异算子来增强算法性能。实验结果表明 ,MPENNs能成功解决异或问题、三元奇偶问题及成品烟的感官质量评价问题。     

Feedback and Feedforward Optimal Control for Offshore Jacket Platforms

The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load that offshore structures are subjected to, and it can be taken as harmonic excitation for the system. The iineafized Morison equation is employed to estimate the wave loading. The main result concerns the existence and design of a realizable optimal regulator, which is proposed to damp the forced oscillation in an optimal fashion. For demonstration of the effectiveness of the control scheme, the platform performance is investigated for different wave states. The simulations axe based on the tuned mass damper (TMD) and the active mass damper (AMD) control devices. It is demonstrated that the control scheme is useful in reducing the displacement response of jacket-type offshore platforms.