基于模糊识别的水资源承载能力综合评价

应用模糊识别模型解决水资源承载能力综合评价问题,根据水资源承载能力相关因子自身的数据结构特性,通过模糊识别模型得到样本的级别特征值,从而达到对待评价区域的识别、归类,同时可以得到各相关因子在评价过程中的贡献率,实例部分将模型应用于全国30个省市、自治区的水资源承载能力评价,结果表明该方法的实用性和客观性。     

平差系统的模型误差及其识别方法研究

论述了模型误差影响参数估值的一些理论问题,指出了随机模型误差和函数模型误差之间的相互作用和转化。为讨论平差系统最优模型的选取,给出了与现有文献将模型误差纳入平差系统的思路不同的一个估计和识别模型误差的理论基础公式,由此导出了相应的实用公式,给出了平差系统模型的优选方法。     

基于PCM改进算法的遥感混合像元模拟分析

混合像元的存在是影响遥感图像分类精度的主要原因,模糊分类是进行混合像元分解的重要方法,其效果的好坏取决于各像元分类后对各类别的隶属度值能否准确地反映像元的类别组成。当非监督分类中的聚类数目与实际类别数目不符,或者监督分类中训练样本存在未训练类别时,常用的模糊c-均值(FCM)方法的效果将大大降低,而可能性c-均值(PCM)方法则可以解决这个问题。该文提出了基于PCM算法的遥感图像混合像元分解方法,并用监督分类方法实例说明PCM方法的优越性。     

A new method for indentifying the model error of adjustment system

Some theory problems affecting parameter estimation are discussed in this paper. Influence and transformation between errors of stochastic and functional models is pointed out as well. For choosing the best adjustment model, a formula, which is different from the literatures existing methods, for estimating and identifying the model error, is proposed. On the basis of the proposed formula, an effective approach of selecting the best model of adjustment system is given. Project supported by the Open Research Fund Program of the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (No. 905276031-04-10).     

Structural damage detection using the optimal weights of the approximating artificial neural networks

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.     

Parameter identification of inelastic structures under dynamic loads

The generalized model of differential hysteresis contains 13 control parameters with which it can curve‐fit practically any hysteretic trace. Three identification algorithms are developed to estimate the control parameters of hysteresis for different classes of inelastic structures. These algorithms are based upon the simplex, extended Kalman filter, and generalized reduced gradient methods. Novel techniques such as global search and internal constraints are incorporated to facilitate convergence and stability. Effectiveness of the devised algorithms is demonstrated through simulations of two inelastic systems with both pinching and degradation characteristics in their hysteretic traces. Owing to very modest computing requirements, these identification algorithms may become acceptable as a design tool for mapping the hysteretic traces of inelastic structures. Copyright © 2002 John Wiley & Sons, Ltd.     

Multi‐objective optimal design of FLC driven hybrid mass damper for seismically excited structures

Structural vibration control using active or passive control strategy is a viable technology for enhancing structural functionality and safety against natural hazards such as strong earthquakes and high wind gusts. Both the active and passive control systems have their limitations. The passive control system has limited capability to control the structural response whereas the active control system depends on external power. The power requirement for active control of civil engineering structures is usually quite high. Thus, a hybrid control system is a viable solution to alleviate some of the limitations. In this paper a multi‐objective optimal design of a hybrid control system for seismically excited building structures has been proposed. A tuned mass damper (TMD) and an active mass driver (AMD) have been used as the passive and active control components of the hybrid control system, respectively. A fuzzy logic controller (FLC) has been used to drive the AMD as the FLC has inherent robustness and ability to handle the non‐linearities and uncertainties. The genetic algorithm has been used for the optimization of the control system. Peak acceleration and displacement responses non‐dimensionalized with respect to the uncontrolled peak acceleration and displacement responses, respectively, have been used as the two objectives of the multi‐objective optimization problem. The proposed design approach for an optimum hybrid mass damper (HMD) system, driven by FLC has been demonstrated with the help of a numerical example. It is shown that the optimum values of the design parameters of the hybrid control system can be determined without specifying the modes to be controlled. The proposed FLC driven HMD has been found to be very effective for vibration control of seismically excited buildings in comparison with the available results for the same example structure but with a different optimal absorber. Copyright © 2002 John Wiley & Sons, Ltd.     

区域水资源开发模式研究

正确的不资源开发模式对于指导合理开发利用区域水资源并最大限度降低环境生态负效应具有重要的意义。以松嫩盆地为例，根据研究区内的水文、水文地质条件，应用模糊集理论与方法确定出松嫩盆地各县市的水资源开发模式，并对各种开发模式做了简要论述。     

应用CP网络进行岩性识别

为通过测井解决岩性识别问题，引入了具有分类准确、算法简练等优点的CP（Counter-Propagation)网络。在详细介绍CP网络的网络模型和算法的基础上，结合某油田的实际测井资料，进行了CP网络识别研究。应用结果表明：CP网络训练周期短、识别准确率高、不存在收敛问题。通过试验研究得出结论：CP网络完全可以用于解决岩性识别等问题，具有广阔的应用前景。     

沉积物物源定量识别的非线性规划模型——以东海陆架北部表层沉积物物源识别为例

依据“质量守衡”原理和沉积物物源难以穷尽的认识 ,提出了沉积物物源定量识别的非线性规划数学模型。利用该模型实现了东海陆架北部表层沉积物物源的定量计算 ,阐明了长江、黄河入海物质的大致分布特征。长江沉积物主要分布于 32°N以南、台湾暖流以西的海域 ,黄河沉积物主要分布于 32°N以北、台湾暖流以东的海域。长江、黄河两类沉积物的这种分布格局与本海域的流系分布相吻合。