神经网络

With introducing the Proportional to Population Size Sampling technique into resample for the problem of specimen reconstruction effectiveness and rationality, a neural network ensemble method based on the PPS technique was proposed, which could improve the accuracy of individual network and the differences among individual networks; and a new dynamic selection approach of individual network in a neural network ensemble was put forward.

针对样本重组的有效性和合理性问题，将PPS抽样技术引入样本重构，提出了基于PPS抽样的集成神经网络算法，以提高个体神经网络的准确性与差异度，并实现动态选择个体神经网络的神经网络集成新方法。

A hybrid model of short-term load forecasting based on chaotic theory, correlation and neural networks is presented in this paper. Firstly, reconstruct attractors in phase spaces using chaotic theory, Secondly fit the attractor's evolvement using BP neural networks, because selecting neural network's input training data using Euclid distance and correlation, improve neural network's associative memory and ratiocinative ability, can better fit the attractor's evolvement.

提出一种将混沌理论、关联度和神经网络相结合的短期负荷预测模型，首先利用混沌理论重构负荷时间序列的相空间吸引子，然后用BP 神经网络来拟合空间吸引子的演化，由于使用空间欧氏距离和关联度联合来选取神经网络的训练样本，这样就提高了神经网络对负荷序列混沌特性的联想和泛化推理能力，能够更好的拟合吸引子的演化。

Firstly, reconstruct attractors in phase spaces using chaotic theory,Secondly fit the attractor s evolvement using BP neural networks, because selecting neural network s input training data using Euclid distance and correlation, improve neural network s associative memory and ratiocinative ability, can better fit the attractor s evolvement.

提出一种将混沌理论、关联度和神经网络相结合的短期负荷预测模型，首先利用混沌理论重构负荷时间序列的相空间吸引子，然后用BP神经网络来拟合空间吸引子的演化，由于使用空间欧氏距离和关联度联合来选取神经网络的训练样本，这样就提高了神经网络对负荷序列混沌特性的联想和泛化推理能力，能够更好的拟合吸引子的演化。

And the structures of ANNs are similar in different hydrologic systems, by this mean, the basic information such as distributing of hydrometric stations can't be utilized. This paper presents a new flood forecast model based on complex ANN, which can make the information of hydrologic systems as guidance when constructing the structure of ANN.

3通过建立复合型型人工神经网络模型的方法，有效的利用给定水文系统的先验知识为人工神经网络模型的建模提供指导，使得建立出的模型更具合理性，该方法不同于传统的人工神经网络建立方法，为基于人工神经网络的洪水预报建模提供了一种新的思路。

After analysis and parison of virtues and hort ings of existing systemsafety evaluation ways, Artificial Neural Network, Artificial Intelligence and Expert System are used to build new system safety evaluation means. System safety evaluation model and intelligent expert system for fault diagnosis based on ANN have been put forth. Development and realization of visual neural network for system safety valuation and fault diagnosis provides new idea for system safety evaluation and accident mode diagnosis.

对已有的系统安全评价技术方法的优缺点进行了分析和对比，继而将神经网络技术、人工智能技术、专家系统应用于开发新的系统安全评价方法，提出了基于人工神经网络的系统安全评价方法和基于神经网络的智能故障诊断专家系统模型，开发了可视化的神经网络系统，为系统安全评价与事故模式诊断提供了新思路。

Artificial neural network is a fast developing intercrossed subject. The theory of ANN make the transmitting of large information and complex calculation possible. ANN is not only a highly non-linear dynamics system but also a self-organization and self-improvement system. In the early 1980's, the appearance of ANN dramatically promote the primary research on understanding and intelligence of human-being as well as the computer industry.

人工神经网络是一门发展十分迅速的交叉学科，神经网络理论是巨量信息并行处理和大规模平行计算的基础，神经网络既是高度非线性动力学系统，又是自适应组织系统。80年代初，神经网络的掘起，已对认知和智力本质的基础研究乃至计算机产业都产生了空前的刺激和极大的推动作用。

This paper first introduces the basic principle and concept of data fusion, summarizes levels and model structure of data fusion, summarizes common algorithms and sorts them, separately expounds data fusion algorithms, neural network and evidence theory, and lucubrates the structure design of neural network. And then, the advantages and disadvantages of neural network and the evidence theory are discussed. In order to colligate the two algorithms strongpoint and increase the fault diagnosis accuracy, fault diagnosis method using data fusion integrated Ann and evidence theory is presented and diagnosis approach of this method is expatiated on.

本文首先对数据融合的基本原理、基本概念进行了阐述，总结了数据融合的层次和模型结构形式，并对常用的融合算法进行了总结分类；分别对数据融合算法—神经网络方法和证据理论方法—进行了详细的阐述，同时对神经网络的结构设计进行了深入研究；然后对神经网络和证据理论方法优缺点进行了分析，在此基础上为了实现两种算法优势互补，提高诊断的准确率，提出了神经网络和证据理论集成的数据融合故障诊断法，并详细阐述了这种方法的诊断原理。

Uncertain upper boundary function is learned on-line by using FNN, and proportion controller strengthens the completeness of FNN control strategy. FNN is trained by using disparity target learning error produced by FIE. This can avoid supersaturating problem made by feedback error directly, and restrain influence of measuring noise and improve control performance.

由于模糊技术与神经网络的结合能弥补彼此的不足，因此本文提出了一种模糊神经网络控制器与比例控制器相结合的控制方案，采用模糊神经网络在线学习不确定函数的上界，比例控制器增强模糊神经网络控制策略的完备性。

The main contents are as follows:Firstly, starting with the general situation of soil erosion and the harms caused by it, the causes leading to the local soil erosion problem are analyzed comprehensively in the paper. And connecting with the measures taking place, sticking points towards the career of soil and water conservation are expatiated upon.Secondly, Back-Propagation Neural Network, One of Artificial Neural Network is used to set up a modal about the connection of the soil erosion modulus and seven factors impacts on it, such as, rainfall, rainfall largest intensity in 30 minutes, runoff coefficient, vegetation cover percent, rate of granule, rate of physical viscidity-clay, the rate of organic matter. Through the comparison with linear regression model, the second regression model, the Chinese Soil Loss Equation, it illustrated that BP Neural Network modal is more accurate than the other three modals in forecasting the mount of soil erosion, and the BP Neural Network will have some applicability in forecasting in soil erosion.

本文以霍山县作为皖西大别山区的典型区域，主要研究了以下内容：(1)从介绍霍山县土壤侵蚀状况以及所造成的危害入手，全面分析了导致当地土壤侵蚀发生的原因，并结合当地采取的水土保持相关措施，阐述了当地水土保持工作的症结所在；(2)结合上土市水土保持试验站多年实测资料和2005年实验资料，应用BP神经网络理论，建立了次降雨土壤侵蚀量与次降雨量、最大30min雨强、径流系数、植被覆盖度等因子之间关系的模型，并通过BP神经网络的预测模型与一次回归模型、二次回归模型、CSLE模型之间的对比分析，说明了建立的BP神经网络模型在土壤侵蚀预测可以取得较回归模型和CSLE模型更高的精度，也说明了BP神经网络理论在土壤侵蚀预报中具有一定的适用性。

The system implemented forest fire model selection automatically and intelligently. BP artificial neural network model of forest fire model selection was build by treating forest fire environment data as inputting variable and treating appropriate forest fire model as outputting variable. At the same time, we studied the methods of acquiring and calculating data of inputting and outputting. The system implemented machine of model selection automatically based on dynamic data driven technology. We selected 72 items experimental data from historical forest fire records in Beijing to experiment and confirm the validity of model selection. It turned out that the reliability of model selection is more than 80 percent.

3基于BP人工神经网络方法设计了林火模型适宜性选择技术框架结构，通过神经网络形成林火模型选择知识，实现了林火模型的自动化和智能化选择；以火场环境因子为输入变量，以适宜火场环境模拟的林火蔓延模型作为输出变量，构建了林火模型选择神经网络模型；研究了输入、输出因子数据的获取与计算方式，实现了动态数据驱动的林火模型自动选择机制；以本京市为例，选择了有详细火场情况记录的72场林火作为实验样本，其中60条记录作为学习样本集，12条记录作为验证样本，对神经网络进行了学习和验证，实验结果表明，模型选择精度可达到80%以上。

Singer Leona Lewis and former Led Zeppelin guitarist Jimmy Page emerged as the bus transformed into a grass-covered carnival float, and the pair combined for a rendition of "Whole Lotta Love".

歌手leona刘易斯和前率领的飞艇的吉他手吉米页出现巴士转化为基层所涵盖的嘉年华花车，和一双合并为一移交&整个lotta爱&。

This is Kate, and that's Erin.

这是凯特，那个是爱朗。