基于人工神经网络的动态过程聚类研究

为了有效解决传统人工神经网络对于时变函数的聚类问题,以及提高在大样本下网络的学习和泛化能力,提出了基于离散余弦变换的传统人工神经网络动态过程聚类方法。通过离散余弦变换将样本函数降维映射到由对应余弦参数所张成的模式特征空间,满足了传统人工神经网络对输入样本的要求,使传统人工神经网络实现动态过程的聚类成为可能。给出了实现算法,分析了计算复杂度,并使用基本竞争型人工神经网络对特征样本向量进行聚类,实验结果表明该方法是正确、有效的。与过程人工神经网络相比,该方法具有运算简单、物理意义明确等优点。     

基于GA和ANN的随机规划模型对储层参数的预测

储层物性参数的分布一直是油藏描述的关键问题和难点。本文采用随机规划与ANN(人工神经网络模型)相结合的建模方法，设计了一种改进的GA(遗传算法)作为随机规划模型的解法，并把这种方法用于储层参数的预测。实验表明该模型具有较强的预测能力和实用性。     

多层前向人工神经网络在东部平原湖泊富营养化评价中的应用

本文通过对传统的误差反向传播前向多层人工神经网络进行改进,形成了改进的多层前向人工神经网络。通过在湖泊水体富营养化各指标浓度间生成正态分布的样本的模式,生成了大量用于人工神经网络模型训练和检验用的样本,并应用该方法所生成的人工神经网络,对我国的4个东部平原湖泊水体富营养化状况进行综合评价。研究表明,应用改进的前向多层人工神经网络方法对湖泊水体进行富营养化评价,结果合理,方法可行,有一定的参考价值。     

摄像机神经网络标定技术

利用人工神经网络来直接学习图像信息与三维信息之间的关系，不需确定摄像机具体的内部参数和外部参数，也不需知道有关模型或参数的先先验知识。在双目视觉的情况下，两摄像机的位置关系不需具体求出，而是隐含在神经网络之中。实验结果表明神经网络方法的标定结果令人满意，并成功地用于机器人曲线跟踪的实验。     

基于规则的核电站故障诊断神经网络系统研制

人工神经网络方法越来越多地用于核电站诊断系统中,但是由于神经网络训练需要大量的训练样本并且诊断过程的不透明性,使得人工神经网络的应用受到限制。论文提出了一种人工神经网络故障诊断系统,结合了人工神经网络、模糊控制技术和专家系统的优点,使诊断过程易于理解,而且能够获得相应的解释,有更大的适应性。     

人工神经网络GTAW建模及控制

给出一种用于钨极气体保护电弧焊（ＧＴＡＷ）建及控制的人工神经网络（ＡＮＮ），重点论述利用ＡＮＮ建立焊接参数模型的方法以及在熔深控制方面的应用，通过实验证明，所提出的智能方法具有良好的系统控制性能。     

BP神经网络用于水体中N综合污染评价

为解决环境质量评价的模式识别问题，采用BP算法的人工神经网络建立水质评价模型，对长江上游某一监测区间的对照断面水体的N元素污染情况进行综合评价。通过实例探讨，这种新的BP网络既适用于定量指标的水质参数的评价又适用于定性指标的水质参数的评价，可以用于环境评价体系的建立。     

基于随机博弈模型的网络攻防量化分析方法

针对日益普遍和多样的网络攻击和破坏行为,如何利用模拟真实网络的虚拟环境,实现对网络各种攻防过程的实验推演,并分析评价网络系统安全性,已逐渐成为热点研究方向.对此文中提出了采用随机博弈模型的网络攻防实验整体架构,提出了由网络连接关系、脆弱性信息等输入数据到网络攻防博弈模型的快速建模方法,基于最终生成的攻防模型可以对目标网络的攻击成功率、平均攻击时间、脆弱节点以及潜在攻击路径等方面进行安全分析与评价. 最后,应用研究所得的网络攻防模型与分析方法对一个典型的企业网络攻防过程进行分析和推演. 结果表明了模型和分析方法的有效性.     

PSO优化的神经网络在教学质量评价中的应用

针对以往教学质量评估体系中存在的问题,利用粒子群优化算法(PSO)训练的神经网络建立教学质量评估数学模型.该方法使用由PSO训练的BP模型来拟合影响教师教学质量评价的众多指标与评价结果之间的复杂关系.实验结果表明,运用人工神经网络能更好的建立综合评价系统,用于满足更多范围的系统综合评价.     

多核机群下基于神经网络的MPI运行时参数优化

多核处理器的新特性给MPI应用带来了新的优化空间,其中调优MPI运行时参数被证明是优化MPI应用的有效方法.然而最优的运行时参数不仅与多核机群的体系结构有关,也决定于MPI应用的程序特征.提出并分析了一种在给定多核机群下基于人工神经网络的优化模型,用于自动为未知的MPI程序预测接近最优的运行时参数.两个不同基准的实验证明了本方法的有效性.实验证明,基于本方法得到的运行时参数所产生的加速比平均达到了实际最大加速比的95%以上.     

Applications of artificial neural networks to the nonlinear combination of forecasts

Abstract: This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods are used to compare with the ANN combining method. The comparative experiment using real-world data shows that the prediction by the ANN method outperforms those by linear combining methods. The paper suggests that the ANN method can be used as an alternative to conventional linear combining methods to achieve greater forecasting accuracy.     

Analysis of a variable speed vapor compression system using artificial neural networks

An artificial neural network (ANN) is a mathematical model that is inspired by the operation of biological neural networks. However, this is typically considered a computational model. An ANN can easily adapt to multiple situations and extract information that is apparently hidden in a system.An ANN can be used in three basic configurations: mapping, auto-association and classification. An ANN in a mapping configuration can be used to model a two port system with inputs and outputs. Therefore, a vapor compression system can be modeled using an ANN in a mapping configuration. That is, some parameters from the compression system can be used for input while other parameters can be used as output. The simulation experiments include the design, training and validation of a set of ANNs to find the best architecture while minimizing over-fitting.This paper presents a new method to model a variable speed vapor compression system. This method accurately estimates the number of neurons in the hidden layer of an ANN. The analysis and the experimental results provide new insights to understand the dependency between the input and output parameters in a vapor compression system, concluding that the model can predict the energetic performance of a variable speed vapor compression system. Additionally, the simulation results indicate that an ANN can extract, from the data sets, information that is implicit in the configuration of the vapor compression system.     

Oil holdup prediction of oil–water two phase flow using thermal method based on multiwavelet transform and least squares support vector machine

Oil holdup of oil–water two phase flow (OWTPF) was measured using thermocouple based on the thermal method. A new model based on least square support vector machines (LSSVM) and multiwavelet transform has been proposed for the first time, which is capable of forecasting oil holdup of oil–water two phase flow. The temperature signal of OWTPF is greatly disturbed by noises from external interference, which results in a limited measurement range of oil holdup. In order to solve the problem, a new signal processing method based on the multiwavelet transform is used. Multiwavelet transform has several scaling functions and corresponding wavelet functions, which can simultaneously achieve orthogonality, symmetry. With ideal performance, noises were removed and actual temperature signal was effectively retained. The fluctuated amplitude signal denoised and total flux of OWTPF were employed as inputs and the oil holdup was used as output of LSSVM model. In order to improve the predictive accuracy and generalization ability of the LSSVM model, a Genetic Arithmetic (GA) has been adopted to determine the optimal parameters of LSSVM model automatically. The experiment results indicate that the performance of LSSVM–GA model outperforms those of artificial neural network (ANN), LSSVM–GA model can be used for estimating the oil holdup of OWTPF with reasonable accuracy.     

重油输送系统基于流量-压力的建模与仿真

应用于阳极焙烧的重油输送的精确控制是一个典型交叉耦合的复杂控制问题，它对炭阳极焙烧质量起关键作用，输送过程中涉及到重油流量、压力和温度三个参量的变化。为了简化所建模型，假定重油输送过程中油温恒定，这样在建模过程中就剩下重油流量与压力之间的耦合关系。为了实现阳极焙烧重油输送的精确控制，通过对阳极焙烧重油输送工作机理的分析。以现场实际采集的数据为基础，采用管道流体传输的物理机理建立对象的模型的方法，提出了一种阳极焙烧萤油输送的基于流量-压力精确控制的数学模型，经仿真验证了该模型的有效性。     

应用人工神经网络确定声波孔隙度

利用声波测井获得的时差求取地层孔隙度是石油测井解释中一项重要任务，传统的方法主要是利用Wyllie实验得到的时间平均公式以及其改进形式或经验公式，均为统计学方法，在具体应用上是很不方便的，优越于统计学理论的人工神经网络方法具有高度的自学习、自适应和抗干扰性等优点，采用带有非线性连接权的二层前馈神经网络能够取代三层BP网络的功能，实际应用表明，应用神经网络能够很好地确定声波孔隙度．     

基于人工神经网络组合预测油田产量

油田原油产量的准确预测可以对油田的生产管理进行合理的指导。该文探讨了应用神经网络组合方法预测油田产量，对开井数、含水率、动用储量以及往年产量同未来产量之间的复杂关系建立模型。采用了两层预测系统：第一层包含两个神经网络，一个多层前馈网络和一个函数链接网络；第二层是把第一层的两个网络输出进行组合。研究了五种不同的组合算法：平均法、最小平方回归法、模糊逻辑法、自适应前馈神经网络法和自适应函数链接神经网络法。根据油品类型分为稀油、热采稠油、常规稠油和总产量四组数据，对上述方法进行了测试，结果表明应用人工神经网络的组合预测方法优于其他的预测方法，而且适用范围广。     

基于神经网络预测模型输入参数配置方法的实现

基于数据挖掘中的关联概念,提出了一种针对神经网络预测模型训练参数的选择方法,有效地提高了神经网络模型在毛纺工艺中对纱线断头率的预测精度;该方法通过生产中的训练参数记录进行关联规则的提取,可快速的排除产生负面影响的训练参数,迅速选择可以提高预测精度的训练参数,从而达到提高神经网络模型预测性能的目的;实验证明,利用关联算法进行参数配置,可以有效提高神经网络输入模型的预测精度.     

Process Parameters Optimization: A Design Study for TiO$_{2}$ Thin Film of Vacuum Sputtering Process

This paper proposes a procedure for process parameters design by combining both modeling and optimization methods. The proposed procedure integrates the Taguchi method, the artificial neural network (ANN), and the genetic algorithm (GA). First, the Taguchi method is applied to minimize experimental numbers and to collect experimental data representing the quality performances of a system. Next, the ANN is used to build a system model based on the data from the Taguchi experimental method. Then, the GA is employed to search for the optimal process parameters. A process parameters design for a titanium dioxide (TiO₂) thin film in the vacuum sputtering process is studied in this paper. The quality objective is to form a smaller water contact angle on the TiO₂ thin-film surface. The water contact angle is 4° obtained from the system model of the proposed procedure. The process parameters obtained from the proposed procedure were used to conduct the experiment in the vacuum sputtering process for the TiO₂ thin film. The water contact angle given from the practical experiment is 3.93°. The difference percent is 1.75% between 4° and 3.93°. The result obtained from the system model of the proposed procedure is promising. Hence, we can conclude that the proposed procedure is a very good approach in solving the problem of the process parameters design.     

A genetic algorithm-based artificial neural network model for the optimization of machining processes

Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal parameters during process planning, design and implementing the adaptive control systems). When knowledge about the relationship among the various parameters of manufacturing are found to be lacking, ANNs are used as process models, because they can handle strong nonlinearities, a large number of parameters and missing information. When the dependencies between parameters become noninvertible, the input and output configurations used in ANN strongly influence the accuracy. However, running of a neural network is found to be time consuming. If genetic algorithm-based ANNs are used to construct models, it can provide more accurate results in less time. This article proposes a genetic algorithm-based ANN model for the turning process in manufacturing Industry. This model is found to be a time-saving model that satisfies all the accuracy requirements.     

基于ANN/HMM的中国手语识别系统

手语是聋哑人使用的语言。它是由手形动作辅之以表倩姿势为符号构成的比较稳定的表达系统,是一种靠动作／视觉交际的特殊的语言。一方面,手语识别可以作为健全人与聋哑人之间的翻译,为聋哑人提供更好的服务;另一方面,作为人体语言理解的一部分,手语识别可作为人机交互的一种手段。该文实现了基于ANN／HMM的手语识别系统,采用ANN方法建立了关于手形、位置、方向的特征映射器,并在建立手形特征映射器的过程中,给出了多特征多分类器融合算法。实验证明,基于ANN／HMM的手语识别系统是可行及实用的。