Forecasting Monsoon Precipitation Using Artificial Neural Networks   总被引：1，自引：0，他引：1  

Xiaodan Wu  Cao Hongxing  Andrew Flitman  Wei Fengying  Feng Guolin 《大气科学进展》2001,18(5):950-958

This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a ＇new＇ data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.  相似文献   

神经网络半主动TLCD对偏心结构的减震控制   总被引：5，自引：0，他引：5  

李宏男  霍林生等 《地震工程与工程振动》2001,21(4):135-141

本文采用在结构水平双向设置TLCD半主动控制装置的方法，对偏心结构在多维地震作用下的振动控制问题进行研究。首先利用多层前向神经网络，对偏心结构在双向地震输入下的两个平动方向的反应进行预测，然后在建立起半主动控制策略的基础上，利用神经网络根据控制准则调整TLCD的开孔率，实现以结构的半主动控制，数值结构表明，这种方法能对结构的平动反应和扭转反应都能起到的较好的减震效果。  相似文献   

基于ART理论的自组织神经网络模型在水资源分类中的应用     

罗先香  邓伟 《吉林大学学报(地球科学版)》2001,31(1):54-57

BP和RBF神经网络技术以其强大的学习功能应用于水资源分类 ,取得了很好的效果。但当不具备已知样本时 ,以上技术很难应用。提出了可塑性较强、无监督的A -K网络模型 ,阐述了其基本原理和算法 ,并将其用于水文水资源研究领域中。实例表明 ,该方法能较理想地解决已知样本的分类问题 ,具有良好的应用前景  相似文献   

基于人工神经网络的航磁信息关联方法的研究     

周东岱  叶水盛 《吉林大学学报(地球科学版)》2001,31(2):189-192

应用人工神经网络方法 ,以航磁数据为样本 ,建立了航磁信息关联的BP人工神经网络模型。对山东省 1∶5 0万航磁水平一阶导数轴向图进行计算机自动关联叠置、提取航磁解释构造轴线的研究。经与垂向二阶导数对比分析 ,证实了该方法给出的模型具有较强的非线性、自适应和学习能力。  相似文献   

地地震多参数BP神经网络预测煤层厚度     

韩万林  张幼蒂  李梁 《煤田地质与勘探》2001,29(4):53-54

依据煤层反射波运动学和动力学特征，提取出了波峰波谷振幅A1、平均频率Fa、主频带能量Qf1、低频带宽能量Qf和峰值频率Fmain等5个地地震特征参数。选取8组学习样本，利用4层BP（Back Propagation)人工神经网络模型，采用动量法和自适应调整的改进算法，训练BP网络，用训练好的BP网络预测煤层厚度。经实例验证，地震多参数BP网络预测煤层厚度精度高，是一种有效的煤厚预测方法。  相似文献   

前馈神经网络隐性单调与泛化能力     

何继善  朱德兵 《物探化探计算技术》2001,23(4):289-294,298

应用前馈人工神经网络对广域单调的两组样本进行了模拟反演，引入单调前馈网络的概念对其权值和阈值定解问题和泛化能力进行了较诉研究。表明前馈人工神经网络是一个表达形式简单的复杂系统，其单调特征是隐性的，而且训练网络的成熟性对样本数量和样本内在规律性有一定依赖。强调了前馈人工神经网络的应用效果，指出单调与复合问题还需进一步深入研究。  相似文献   

River flow mass exponents with fractal channel networks and rainfall     

Brent M. Troutman  Thomas M. Over   《Advances in water resources》2001,24(9-10)

An important problem in hydrologic science is understanding how river flow is influenced by rainfall properties and drainage basin characteristics. In this paper we consider one approach, the use of mass exponents, in examining the relation of river flow to rainfall and the channel network, which provides the primary conduit for transport of water to the outlet in a large basin. Mass exponents, which characterize the power-law behavior of moments as a function of scale, are ideally suited for defining scaling behavior of processes that exhibit a high degree of variability or intermittency. The main result in this paper is an expression relating the mass exponent of flow resulting from an instantaneous burst of rainfall to the mass exponents of spatial rainfall and that of the network width function. Spatial rainfall is modeled as a random multiplicative cascade and the channel network as a recursive replacement tree; these fractal models reproduce certain types of self-similar behavior seen in actual rainfall and networks. It is shown that under these modeling assumptions the scaling behavior of flow mirrors that of rainfall if rainfall is highly variable in space, and on the other hand flow mirrors the structure of the network if rainfall is not so highly variable.  相似文献   

Neuro‐control of seismically excited steel structure through sensitivity evaluation scheme     

Dong‐Hyawn Kim  In‐Won Lee 《地震工程与结构动力学》2001,30(9):1361-1377

The neuro‐controller training algorithm based on cost function is applied to a multi‐degree‐of‐freedom system; and a sensitivity evaluation algorithm replacing the emulator neural network is proposed. In conventional methods, the emulator neural network is used to evaluate the sensitivity of structural response to the control signal. To use the emulator, it should be trained to predict the dynamic response of the structure. Much of the time is usually spent on training of the emulator. In the proposed algorithm, however, it takes only one sampling time to obtain the sensitivity. Therefore, training time for the emulator is eliminated. As a result, only one neural network is used for the neuro‐control system. In the numerical example, the three‐storey building structure with linear and non‐linear stiffness is controlled by the trained neural network. The actuator dynamics and control time delay are considered in the simulation. Numerical examples show that the proposed control algorithm is valid in structural control. Copyright © 2001 John Wiley & Sons, Ltd.  相似文献   

热带气旋暴雨增幅造成北方特大暴雨的预报   总被引：1，自引：0，他引：1  

曹钢锋  朱官忠  张善君 《气象》1996,22(3):3-7

利用人工神经元网络的非线性决策特性，采用前向多层的误差反向传输网络，研制了一个登陆北上热带气旋暴雨增幅造成北方特大暴雨的预报方法－－能自动判虽有无暴雨增幅及其出现地区的人工神经元分类预报网络。该方法简便客观，试用效果较好，即可投入业务使用。  相似文献   

监测网优化设计的必要性及内容     

周世健  陈永奇 《四川测绘》1996,19(1):15-17

本文详细分析了监测网优化设计的必要性，并针对监测网的特点，叙述了监测网设计的有关问题。  相似文献