反扩散格式在二维锋生数值模拟中的应用

把无粘流中能成功捕捉接触间断的反扩散差分格式，用在了准地转二维理想锋生的数值模拟中，并将计算结果与Cullen的计算结果和半地转方程的精确解进行了比较。用反扩散格式模拟的锋面较Cullen的结果更加显，而且更接近半地转方程的精确解。     

南疆西部山区一次特大暴雨特征分析

通过对2001年9月22－23日南疆西北部山区特大暴雨的环流背景、物理量场、卫星云图分析，揭示了这场特大暴雨的物理机制和各要素场特征。     

吐鲁番山区人工增水作业效果的统计分析

应用统计检验和回归分析两种数理统计方法，对吐鲁番1997－2000年山区人工增水作业效果进行了分析。结果得出作业影响区内作业年份5－6月平均流量显著增加。     

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.     

Modelling evaporation using an artificial neural network algorithm

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.     

Simulation of artificial random field considering the temporal-spatial variation

Introduction For the seismic design of special structures such as nuclear power station, marine platform, long-span bridge and dam, generally the time-history response analysis of the structure under seismic excitation is imperative, which was coded in most seismic design codes. The earthquake records suitable for the seismic situation and site condition are necessary to be used as the seismic input in the dynamic analysis of structures. As a result of the limited observational condition of st…     

Application of genetic BP network to discriminating earthquakes and explosions

Introduction Artificial Neural Network (ANN) is an important branch of artificial intelligence. It is proposed on the foundation of the study on modern neural science, is a man-made network that can implement some functions based on the mans comprehensive understanding for cerebral neural network (HAN, WANG, 1997). ANN is a mathematical model of simplified human brain neural network and is used to simulate the structures and functions of human brain neural network. ANN is a complex netw…     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.     

固体介质中弹性波传播机制的细胞自动机有限深势阱模型

分析了采用细胞自动机研究波动问题的建模方法，针对一维、均匀、各向同性固体介质中弹性纵波的微观机制，借用一经典弹簧振子模型、细胞自动机格子气模型，以及量子力学中的无限深势阱模型，建立了一个细胞自动机有限深势阱模型，从量子力学角度出发，基于介观物理和纳米概念，以微观精子的德布罗意假设为基础，利用薛定谔方程，讨论了该模型中粒子（分子组）的振动速度与粒子物质波波速之间的联系，给出了模型中的波动方程，得出ζ＝Vp（ζ为粒子振动速度，Vp为物质波纵波波速）。同时还讨论了模型中粒子的大小和能量传递问题，引入引力场，得出了能量及引力势的量子化条件，另外，对声波速度、格子气粒子振动速度和本文模型中分子组振动速度进行了比较；还对本文模型中的粒子能量分布作了分析。     

钢筋砼摩擦耗能支撑框架结构的单元刚度和力学模型分析

为研究钢筋砼摩擦耗能支撑框架结构的动力反应性能 ,对其中的摩擦耗能器单元和框架杆单元的单元刚度和力学模型做了分析。钢筋砼摩擦耗能支撑单元由支撑杆单元和钢板—橡胶摩擦耗能器单元组成 ,支撑单元可取空间杆单元 ,摩擦耗能器单元为平面应力矩形单元。摩擦耗能器单元的剪切恢复力曲线为理想的弹塑性曲线 ,根据耗能器单元的力学模型 ,可确定其在每一时刻的刚度 ;框架结构空间杆单元的恢复力模型采用双线型模型 ,根据杆单元的力学模型 ,可确定其在每一时刻的刚度。并利用所编制的程序对十层单榀两跨空间普通框架和摩擦耗能支撑框架在地震作用下进行了弹塑性反应时程分析 ,结果表明耗能支撑框架的顶层最大位移明显小于普通框架