受扰线性系统的前馈-反馈最优控制

针对具有外部持续扰动的线性系统，研究前馈-反馈最优控制律的设计问题。给出了最优控制律的存在唯一性条件。并提出了最优控制律的设计算法。利用滤波器解决了前馈控制的物理不可实现问题。仿真结果表明，此算法易于实现，与传统的反馈最优控制相比对抑制外部扰动具有较强的鲁棒性。     

Sensitivity Analysis of a Feedforward Neural Network for Considering Genetic Mechanisms of Kuroko Deposits

Exploration for volcanogenic massive sulfide deposits of the kuroko-type is underway in many places. Clarifying the spatial patterns of the metals in kuroko deposits will be useful for understanding their genetic mechanisms and for future exploration of such types of deposits. This study represents a spatial distribution analysis on the contents of principal metals of kuroko deposits: Cu, Pb, and Zn, in the Hokuroku district, northern Japan, by a feedforward neural network and 1917 sample data at 143 drillhole sites. The network, which consists of three layers, was trained by the principle of SLANS in which the numbers of neurons in the middle layer and training data are changed to improve estimation accuracy. Using the weight coefficients connecting adjacent neurons, sensitivity analysis of the neural network was carried out to identify factors influencing spatial distributions of the three metals. The coordinates depth (z) direction, Bouguer gravity, and specific lithology such as dacite were determined to be influencing factors. The high frequency of the z coordinate signifies that the metal contents differ to a large extent by depth. The sensitivity vector was defined using sensitivity coefficients for x, y, and z coordinates of an estimation point. We determined that the directions of large vectors were different inside and outside of the Hanawa-Ohdate area. This characteristic is considered to originate from the differences in the permeability of fractures that became the paths for rising ore solutions, and the depths that the solutions mixed with sea water.     

Application of a neural network model in establishing a stage–discharge relationship for a tidal river

This paper presents the applicability of neural network (NN) modelling for forecasting and filtering problems. The multilayer feedforward (MLFF) network was first constructed to forecast the tidal‐level variations at the mouth of the River Chao Phraya in Thailand. Unlike the well‐known conventional harmonic analysis, the NN model uses a set of previous data for learning and then forecasting directly the time‐series of tidal levels. It was found that lead time of 1 to 24 hourly tidal levels can be predicted successfully using only a short‐time hourly learning data. The MLFF network was further used to establish a stage–discharge relationship for the tidal river. The results show a considerably better performance of the NN model over the conventional models. In addition, the stage–discharge relationship obtained by the NN model can indicate reasonably well the important behaviour of the tidal influences. Copyright © 2003 John Wiley & Sons, Ltd.     

A simple strategy for dynamic substructuring: I. Concept and development

Dynamic substructuring refers to physical testing with computational models in the loop. This paper presents a new strategy for such testing. The key feature of this strategy is that it decouples the substructuring controller from the physical subsystem. Unlike conventional approaches, it does not explicitly include a tracking controller. Consequently, the design and implementation of the substructuring controls are greatly simplified. This paper motivates the strategy and discusses the main concept along with details of the substructuring control design. The focus is on configurations that use shake tables and active mass drivers. An extensive experimental assessment of the new strategy is presented in a companion paper, where the influence of various factors such as virtual subsystem dynamics, control gains, and nonlinearities is investigated, and it is shown that robustly stable and accurate substructuring is achieved.     

基于改进型前馈神经网络的流域产流预报模型的研究

在分析流域产流机制、影响因素和现行产流计算方法的基础上,首次取前期影响雨量、主产流历时、全过程面平均雨量和４个代表雨强计７个因子作为神经网络输入,直接以流域产流深作为神经网络输出,并针对传统ＢＰ算法的固有缺陷,采用混合ＧＮ－ＢＦＧＳ算法训练网络。实例验证了所建模型及算法的有效性和可行性。还对神经网络隐层单位数等进行了初步研究。     

水库群优化调度函数的人工神经网络方法研究

提出了研究水库群优化调度函数的人工神经网络方法,并探讨了神经网络的训练参数、训练方法和训练样本的改变对网络训练和应用效果的影响。实例研究表明,模型及其算法是可行的、有效的。     

随机波浪荷载作用下海洋平台的前馈-反馈振动控制研究

该文研究线性前馈 -反馈控制策略对海洋平台振动控制的有效性。线性前馈 -反馈控制只有当输入荷载为白噪声过程时 ,所实施的控制才是最优的 ,因此该文采用一白噪声过程通过滤波器来近似随机波浪力谱 ,并将海洋平台 -主动控制系统的动力学方程转化为符合随机最优控制要求的增广状态空间表达形式。依据设计目标中对安全性以及经济性的权衡 ,通过使二次型控制目标函数最小化 ,推导出了随机最优控制力的计算方法 ,从而实现了最优控制的目的。在频率域上分析了海洋平台受控后的振动响应 ,结合典型的海洋天然气生产平台算例 ,将前馈 -反馈控制与反馈控制、TMD控制加以比较 ,总结了前馈 -反馈控制的特点及其优越性     

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.     

Model-free estimation of fracture aperture with neural networks

The feedforward backpropagation technique provides a model-free estimation with neural networks. The algorithm was used to estimate fracture aperture of natural fractures in three dimensional space. A three-layer neural network with at least 5 nodes in a hidden layer was trained on a data set consisting of formation imaging microscanner logs (FMS)from horizontal boreholes. Sensitivity studies were performed to account for the rate of learning convergence, convergence to local error minima, etc. Among the factors contributing mostly to the overall good or bad performance of the network, the following are worth mentioning: number of data points, data spacing, and data variability. It is shown that a smoothing operation applied to aperture data along the wellbore often helps to reduce disorientation of the network and to switch from oscillations or chaotic jumps to convergence.     

Neural Network-Based Estimation of Principal Metal Contents in the Hokuroku District,Northern Japan,for Exploring Kuroko-Type Deposits

The Hokuroku district, extending over 40 × 40 km² in northern Japan, is known to be dominated by kuroko-type massive sulfide deposits that have a genetic relation to submarine volcanic activity. The deposits are hosted in a specific stratigraphic zone of Miocene volcanic rocks. Because kuroko-type deposits are under exploration in several countries, it is important to integrate the geologic and geochemical data that have been accumulated in the Hokuroku district to characterize the distribution of deposits and produce a map of mineral potential. Thus, we collected data on multiple chemical components from 1917 rock cores at 143 drillhole sites and concentrated on components with relatively large amounts of data, which are SiO₂, Al₂O₃, and Fe₂O₃ as major elements and Cu, Pb, and Zn as trace elements. Although frequencies of these data can be approximated by normal or lognormal distributions, spatial correlation structures cannot be extracted from the semivariograms of each component nor from the cross-semivariograms between two components of the major or minor elements. To handle such complexity, a spatial method of modeling content distribution, SLANS, is developed by applying a feedforward neural network. The principle of SLANS is to train a network repeatedly to recognize the relation between the data value and the location and lithology of a sample point. One-hundred outputs for each element are obtained by changing the numbers of neurons in a middle layer from 1 to 10 and sample data used for training from 3 to 12, and finally one output is selected based on the estimation precision of the network which is restricted near the target point. After constructing a geologic distribution model from the geological column classified into 25 rock codes, three-dimensional distributions of Cu, Pb, and Zn contents are estimated over the study area. The content models are considered to be valid because high-content zones are located on the known mine sites and the margins of ancient volcanoes or calderas. Some zones are distributed along strikes of major deep-seated fractures in the district.