智能神经元网络时间序列预测模型的研究

人工神经元网络的研究技术在理论和实际应用上已经比较成熟,在信号处理系统中也采用该技术进行非线性时间序列信号的预测分析。但是由于该理论黑箱模型的特点,无法引入先验知识,从而预测精度难以提高。针对该问题,文中提出了智能神经网络的动态预测模型,引入智能神经元,建立区别于传统神经网络的预测模型,达到了较为理想的预测效果。并以工业生产参数的时间序列预测——某油井生产过程中MinCurrent参数值,作为实验模型,对该方法进行了验证,结果表明了该模型预测精度较高、计算速度快。     

基于径向基过程神经网络的油田开发指标预测

目前为止,现有的油田开发指标预测方法难以反映实际存在的时间累积效应对该指标预测的影响。因此,为提高油田开发指标预测的准确度,本文提出基于径向基过程神经元网络的油田开发动态指标预测模型,并将其应用到实际油田开发动态指标的预测中。实例分析结果表明,本文提出的径向基过程神经元网络的油田开发动态指标的预测方法精度高、速度快,是预测油田开发指标的一种较实用的方法。     

基于过程神经元网络的动态预测模型及其应用

〗针对动态系统过程预测预报问题，提出了一种基于过程神经元网络的动态预测方法．过程神经元网络的输入/输出均可以是时变函数，其时空聚合运算和激励可同时反映时变输入信号的空间聚合作用和输入过程中的阶段时间累积效应．基于过程神经元网络的动态预测模型能同时满足对系统的非线性辨识和过程预测，在机制上对动态预测预报问题有较好的适应性．文中给出了基于函数基展开和梯度下降法的学习算法，以电力负荷预报为例验证了模型和算法的有效性．     

一种基于量子粒子群的过程神经元网络学习算法

针对过程神经元网络模型学习参数较多,正交基展开后的BP算法计算复杂、不易收敛等问题,提出了一种基于双链结构的量子粒子群学习算法.该算法用量子比特构成染色体,对于给定过程神经元网络模型,按权值参数的个数确定量子染色体的基因数并完成种群编码,通过量子旋转门和量子非门完成个体的更新与变异.算法中每条染色体携带两条基因链,提高了获得最优解的概率,扩展了对解空间的遍历,从而加速过程神经元网络的优化进程.将经过量子粒子群算法训练的过程神经元网络应用于Mackey-Glass混沌时间序列和太阳黑子预测,仿真结果表明该学习算法不仅收敛速度快,而且寻优能力强.     

基于QPSO和极限学习的离散过程神经网络及学习算法

连续过程神经元网络在权函数正交基展开时, 基函数个数无法有效确定, 因此逼近精度不高. 针对该问题, 提出一种离散过程神经元网络, 使用三次样条数值积分处理离散样本和权值的时域聚合运算. 模型训练采用双链量子粒子群完成输入权值的全局寻优, 通过量子旋转门和非门完成种群进化. 局部使用极限学习, 通过Moore-Penrose广义逆计算输出权值. 以时间序列预测为例进行仿真实验, 结果验证了模型的有效性, 且训练收敛能力和逼近能力都有一定程度的提高.     

时间序列预测方法综述

时间序列是按照时间排序的一组随机变量,它通常是在相等间隔的时间段内依照给定的采样率对某种潜在过程进行观测的结果。时间序列数据本质上反映的是某个或者某些随机变量随时间不断变化的趋势,而时间序列预测方法的核心就是从数据中挖掘出这种规律,并利用其对将来的数据做出估计。针对时间序列预测方法,着重介绍了传统的时间序列预测方法、基于机器学习的时间序列预测方法和基于参数模型的在线时间序列预测方法,并对未来的研究方向进行了进一步的展望。     

基于离散过程神经元的乙烯生产装置软测量

针对传统M-P神经网络模型的时间依赖性问题,提出将离散过程神经元应用到乙烯裂解炉软测量中,并将Fletcher-Reeves修正的改进变梯度学习算法应用到离散过程神经元网络,达到提高过程神经元网络的训练速度的目的。最后用乙烯装置的生产数据进行仿真研究,仿真结果表明该改进算法具有明显的快速收敛性,实现了乙烯产率的预测。     

基于动态挖掘的流程工业产品质量模型的研究与应用

针对目前流程工业产品质量控制采用人工检查和统计质量控制方法,缺乏产品质量预先预报以及实时调整生产过程的工艺参数存在困难的情况,利用生产过程采集到的实际数据,采用基于时间序列的动态数据挖掘技术和BP神经元网络,预测产品质量和分析产品生产过程的工艺,最大限度减少不合格产品的产出,提高生产效率.根据上述理论建立流程工业数据动态质量控制平台,以中厚钢板生产过程为例,应用表明了这种方法在实际应用中的正确性和有效性.     

流程工业中基于混沌时间序列的成本预测研究

针对流程工业中连续性生产过程的时间序列特点,采用基于混沌时间序列的Lyapunov指数计算和预测方法对成本进行了预测研究。     

基于离散过程神经元的乙烯生产装置软测量

针对传统M—P神经网络模型的时间依赖性问题,提出将离散过程神经元应用到乙烯裂解炉软测量中,并将Fletcher—Reeves修正的改进变梯度学习算法应用到离散过程神经元网络,达到提高过程神经元网络的训练速度的目的。最后用乙烯装置的生产数据进行仿真研究,仿真结果表明该改进算法具有明显的快速收敛性,实现了乙烯产率的预测。     

离散过程神经网络在时间序列预测中的应用

为解决复杂时间序列的预测问题,针对目前过程神经网络的输入为多个连续的时变函数,而许多实际问题的输入为多个序列的离散值,提出一种基于离散输入的过程神经网络模型及学习算法;并以太阳黑子数实际数据为例对太阳黑子数时间序列进行预测,仿真结果表明该模型具有很好的逼近和预测能力。     

基于神经网络的某超市水产品运营量预测模型研究

随着人工神经网络技术的不断成熟,人工神经网络和预测预报紧密结合起来。结合＂神经网络＋预测＂的开发模式,引入BP人工神经网络学习算法,构建了某超市水产品运营预测模型,并讨论不同结构的BP网络及随机初始化对预测结果的影响,同时进行了神经网络预测方法和其它预测方法的比较。     

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.     

基于小波和支持向量机的多尺度时间序列预测

介绍了相空间重构和基于支持向量机的时间序列预测建模技术,提出了基于小波和支持向量机的复杂时间序列预测方法,利用小波对复杂时间序列进行多尺度分解,对重构后的近似序列和细节序列分别利用支持向量机进行回归预测并将结果融合。对股票数据进行预测,试验结果表明该方法预测精度高于单尺度支持向量机和神经网络预测方法,可用于复杂非平稳时间序列的预测。     

周相似特性下交通流组合预测方法研究

根据交通流量具有周相似的特性,构造了周相似序列。用霍特指数平滑法对周相似序列进行预测,用人工神经网络对残差部分进行预测。将指数平滑法与神经网络法相结合,以便发挥每种方法的优势,获得比单个方法更好的预测结果。实例分析表明,比单独使用ARIMA或单独使用神经网络方法,使用组合方法的预测误差最小,适合于实时的交通流预测。     

The influence of ARIMA-GARCH parameters in feed forward neural networks prediction

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg–Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN’s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.     

无线局域网业务流预报方法比较

介绍了基于时间序列、神经网络和小波的多种网络业务的预报方法,应用真实的无线局域网业务流序列检验了这些模型的预报性能,结果表明,和其他预报模型相比,基于神经网络的模型能够比较精确地捕获无线局域网业务流自身的特性,对业务流具有良好的预报性能,而基于ARIMA模型的预报性能最差。     

A non-symbolic implementation of abdominal pain estimation in childhood

The abdominal pain is a very common disease in childhood, which lurks complications. Pediatric surgeons have to estimate at least 15 clinical and laboratory factors in order to make a diagnosis and decide about performing a surgical operation of the abdomen. Artificial Neural Networks (ANNs) are particular implementations of Artificial Intelligence (AI) systems and they are used in a wide area of application fields. This study examines the implementation of ANN architectures, using Multi-Layer Perceptron (MLP) neural networks and Probabilistic Neural Networks (PNN) architectures, in order to specify the appropriate ANN structure for abdominal pain estimation in childhood. The architecture with the best performance is a fully interconnected MLP neural network with an input layer of 15 nodes, one hidden layer of 5 neurons and an output layer, with error back-propagation algorithm being used as the learning scheme. In the output layer, the estimation of appendicitis’ stage is reached automatically. The proposed ANN achieved a percentage of 88.5% of correct classification on testing set cases. Further analysis of obtained results, exhibited the ability of ANN for distinguishing the necessity of a case for operative treatment of abdominal pain based on diagnostic features, attaining a percentage of 100% of successful prognosis over the cases of testing set. The aim of proposed MLP neural network is to assist surgeons in appendicitis prediction, avoiding an unnecessary operative treatment.     

FUZZY PNN ALGORITHM AND ITSAPPLICATION TO NONLINEARPROCESSES

Abstract

In this paper, a fuzzy Polynomial Neural Network (PNN) algorithm is proposed to estimate the structure and parameters of fuzzy model, using the PNN based on Group Method of Data Handling (GMDH) algorithm. The new algorithm uses PNN algorithm and fuzzy reasoning in order to identify the premise structure and parameter of fuzzy implications rules, and the least square method in order to identify the optimal consequence parameters. Both time series data for the gas furnace and data for the NO_x emission process of gas turbine power plants are used for the purpose of evaluating the performance of the fuzzy PNN. The simulation results show that the proposed technique can produce the fuzzy model with higher accuracy and feasibility than other works achieved previously. This algorithm will be applied to limited data processes with several inputs.     

Improved estimation of electricity demand function by using of artificial neural network,principal component analysis and data envelopment analysis

Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod–Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption.