ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS

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

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.     

西苕溪流域不同土地类型下氮元素输移过程

以西苕溪流域为研究对象,选择最有代表性的5种土地类型,模拟天然大暴雨,通过3次重复实验研究不同形态氮素随暴雨径流及径流沉积物的迁移过程,估算氮素在流域内不同土地利用/土地覆被条件下的损失率。研究结果表明,在相同的降雨条件下,氮素的流失速率和流失量随土地利用/土地覆被类型的不同表现出明显差异,地表径流水相总氮的流失量桑林最大,水田最小。水相不同形态的氮素流失量亦有所不同,悬浮颗粒态氮占地表径流水相总氮的70 %~90 %,水相溶解态氮的流失量以松林为最高,竹林、桑林和水田接近而且较低,不同类型的水相溶解态氮也随土地利用类型的不同表现出各自的特征。各土地类型单位面积、表层10 cm土壤氮素流失高达4.66~9.40 g·m-2,其中随径流沉积物相迁移的氮素占绝大部分(90 % 以上)。估算出的各土地利用类型总氮流失速率,地表径流水相为2.68~14.48 mg·m-2·min-1,径流沉积物相高达100.01~172.67 mg·m-2·min-1。     

沙坡头人工植被固沙区天然降水的入渗和分配研究

土壤、植被冠层与大气界面间(SVAT)物质传输过程日益成为水文学研究最感兴趣的领域,降水量的迁移与转换是非灌溉区SVAT主要的物质传输过程.干旱半干旱区稀疏灌丛蒸散量占降水量的90%以上,因此对降水入渗与水分在土壤内植物根际区再分配的研究显得十分重要.试验于2001年8月17日至9月30日在中国科学院沙坡头试验站进行,主要观测人工植被荒漠灌木柠条(Caragana korshinskii) 灌丛固定沙丘降水入渗与再分配过程.结果表明:在7次不连续降水过程中,土壤入渗深度与降水强度呈简单线性相关关系,土壤入渗速率约为降水强度的10倍.当次降水过程中降水强度小于0.46 mmh-1时,土壤入渗速率约为0 cm*h-1,此时的降水对沙区土壤基本上没有水分补给作用.受荒漠灌木柠条根系吸水作用的影响,其根系密集剖面深度40～140 cm内降水水分入渗积累不明显.降水入渗速率及入渗深度受土壤剖面初始含水率多寡而变化,干燥土壤剖面有助于提高瞬时入渗速率.降水以后随着时间的推移,人工固沙区微环境内空气温度、湿度等气象条件适宜,柠条生长进入相对旺盛阶段,其根系密集层140 cm深度处土壤含水率在总体上下降的过程中,表现出昼消夜长的趋势,试验期间翌日 8:00时土壤含水率值略高于前一日20:00时水分值 0.1%～0.3%.     

沙坡头人工植被区中的油蒿种群动态与稳定性

利用种群生态学中的年龄结构、静态生命表和种群动态指数对沙坡头包兰铁路北侧1964年和1981年沙地人工植被区中的油蒿种群结构动态进行分析,发现1981年区和1964年区的油蒿种群都属于增长型,但1981年区的增长性大于1964年区,也就是说,随着人工植被建立的时间延长,油蒿种群结构由快增长型转向慢增长型.因此,沙地中的油蒿种群的动态趋势是由快增长向慢增长,以及衰退方向演变,最终有从人工植被区中消失的趋势.同时,油蒿种群结构的增长型是油蒿能长期存在的种群生态学原因,是它具有适应干旱、半干旱区的沙地生境的生物学特性所决定的.     

腾格里沙漠人工固沙植被区生物土壤结皮对降水的拦截作用

在沙坡头人工植被区对人工模拟降水及天然降水后生物土壤结皮层含水率进行了动态定位监测,并分析了生物土壤结皮拦截降水的作用.结果表明:①生物土壤结皮的发育改变了原来沙丘剖面的水分分配格局,10%～40%的年降水量被拦截到结皮层;②随固沙年限的增加,生物土壤结皮的进一步发育和演变,其对降水的拦截能力也进一步提高;③生物土壤结皮对降水的拦截有明显的季节变化,7～10月份平均拦截雨量比4～6月份平均高出12%.