基于MATLAB的BP神经网络预测系统的设计

利用MATLAB设计了BP神经网络预测系统.介绍了MATLAB的BP神经网络工具箱函数和图形用户界面,详细介绍了BP神经网络预测系统的设计,并对所设计的预测系统进行了性能评价.系统具有良好的性能,在很多领域可以发挥较大的作用.     

基于LabVIEW与人工神经网络的发动机故障诊断研究

简述了人工神经网络的模型。利用LabVIEW8．2,以MatlabScript节点形式调用在Matlab6．5里训练成功后的BP神经网络程序。通过对发动机常见故障的诊断,表明虚拟仪器与人工神经网络的结合可以快速、准确的诊断出故障。     

基于MATLAB的BP神经网络实现研究

BP神经网络是人工神经网络中的一个典型代表。MATLAB的神经网络工具箱提供了许多有关神经网络设计、训练和仿真的函数和方便、友好的图形用户界面来实现BP网络,还可实时将仿真结果可视化,从而使应用BP网络来解决许多领域的实际问题变得非常方便和有效。     

BP神经网络在水质参数预测中的应用

本文利用BP神经网络良好的非线性映射特性,建立了水质预测模型,分别建立三种不同训练算法的氨氮预测神经网络,利用MATLAB神经网络工具箱编程实现。选用大量数据,反复加以训练,通过比较,挑选拟合程度好、精度误差小、泛化性能优秀的预测神经网络,并根据测试样本加以检验分析,最终选定L-MBP网络作为氨氮含量神经网络预测方案。     

基于RBF神经网络的短期负荷预测

短期电力负荷预测是电力系统运行调度中一项重要的内容,传统的电力负荷预测方法都是建立在线性假设基础之上,由于预测精度低,难以满足现在电力部门的要求。人工神经网络己被应用在电力负荷预测中,并取得了较为理想的结果。主要基于神经网络的负荷预测模型,通过MATLAB仿真实验平台,构建RBF神经网络模型,并用历史电力负荷数据进行训练,成功的进行了电力系统的短期负荷预测,预测结果误差较小,取得了令人满意的结果。     

引黄灌渠斗口流量神经元网络软测量模型

鉴于训练后的人工神经网络可以精确逼近任意非线性函数,建立了引黄灌渠斗口流量BP网络和RBF网络软测量模型,精选水工试验数据构成训练样本集,在MATLAB6．5环境下进行了仿真训练。检验表明,基于人工神经网络的软测量模型的输出值与标准三角量水堰的测量结果(期望值)吻合情况良好,水流量测量精度大为改善。     

一种与文本有关的说话人识别系统设计与实现

该文设计并实现了一种基于小波变换和线性预测的语音信号特征提取方法(DWT—LPC)和自组织神经网络的与文本有关的说话人识别系统。该系统包括端点检测、特征提取、说话人识别、网络训练和神经网络参数五部分,结构简单,实时性能好,并且网络训练所需数据少。用MATLAB对说话人识别系统进行仿真实验,选择由元音、轻辅音和浊辅音组成的本文,经过测试,识别率达98.5％。     

基于改进BP神经网络的函数逼近性能对比研究

为了正确反映实际应用中经常采用的6种典型BP神经网络的改进算法的非线性函数逼近能力,本文从数学角度详细阐述这6种典型BP神经网络的改进算法的学习过程,简要地介绍MATLAB工具箱中设计BP网络的训练函数,最后在MATLAB环境下设计具体的网络来对指定的非线性函数进行逼近实验,并对这6种典型BP神经网络的改进算法的性能差异进行对比。仿真结果表明,对于中小规模网络而言,LM优化算法逼近性能最佳,其次是拟牛顿算法、共轭梯度法、弹性BP算法、自适应学习速率算法和动量BP算法。     

基于人工神经网络的工业供应链销售预测方法

为解决工业供应链中存在的精度低、非智能以及无法处理复杂样本的问题,提出一种基于改进人工神经网络的销售预测方法.以加拿大某机电产品销售公司的真实销售数据作为输入样本,利用基于实验数据改进的人工神经网络进行学习训练,进行销售预测,将结果与未改进的人工神经网络和较先进的卷积神经网络和高斯混合模型以及销售公司的销售数据作比较,从准确率、召回率和F值三个指标分析改进人工神经网络的预测精度.实验结果表明,改进后的人工神经网络在三个指标方面均表现出更好的性能,能够较好地预测销售成交情况.     

基于再生核RBF神经网络的瓦斯突出预测系统

该设计的瓦斯突出预测系统由数据采集,数据传输和数据处理三部分组成;首先使用层次分析法和MATLAB选择出了瓦斯突出影响因素,然后使用TMS320C6713和PCI总线技术设计了数据采集和传输系统,同时采用再生核算法来进行RBF神经网络的训练,通过W12[a,b]空间插值逼近的方法,把RBF神经网络的训练转换为解线性方程组,最后使用LABVIEW,MATLAB和CCS混合编程实现了再生核RBF神经网络的训练和仿真以及TMS320C6713软件开发,准确地预测出了瓦斯突出。     

An artificial neural network for prediction of gas holdup in bubble columns with oily solutions

Gas holdup in a bubble column reactor filled with oil-based liquids was estimated by an artificial neural network (ANN). The ANN was trained using experimental data from the literature with various sparger pore diameters and a bubbly flow regime. The trained ANN was able to predict that the gas holdup of data did not seen during the training period over the studied range of physical properties, operating conditions, and sparger pore diameter with average normalized square error <0.05. Comparisons of the neural network predictions to correlations obtained from experimental data show that the neural network was properly designed and could powerfully estimate gas holdup in bubble column with oily solutions.     

Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach

An artificial neural network (ANN) based approach is presented for the assessment of damage in prestressed concrete beams from natural frequency measurements. The details of an experimental programme suitably designed and carried out to induce the desired extents of damages in the prestressed concrete beams and generate the training and test data for the ANN are presented. The analysis of the static and dynamic behavior of perfect and damaged prestressed concrete beams reveal that there exists a close relationship among the natural frequency, deflection, crack width, first crack load, ultimate load and degree of damage. Therefore, these parameters were mainly used as input data for training and testing the ANN. A feed forward ANN learning by back propagation algorithm implemented using MATLAB has been employed in this study. The main focus of this work has been to study the feasibility of using an ANN trained with only natural frequency data to assess the damage in prestressed concrete beams. This is explored by comparing the performance of an ANN trained only with natural frequency data with other ANNs trained with a mix of static and dynamic data. It has been demonstrated that an ANN trained only with dynamic data can assess the damage with less than 10% error, when the error is the difference between the actual damage in percent and predicted damage in percent. The shortcomings of this study have also been presented.     

Flow forecast by SWAT model and ANN in Pracana basin,Portugal

This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the ANNs were applied to the daily flow of the Pracana basin in Portugal. The comparison of ANN models and a process-based model SWAT was established based on their prediction accuracy. The ANN model was found to be more successful than the SWAT in relation to better forecast of peak flow. Nevertheless the SWAT model results revealed a better value of mean squared error. The results of this study, in general, showed that ANNs can be powerful tools in daily flow forecasts.     

基于Matlab的SCM822H齿轮钢性能预测

采用Matlab的人工神经网络工具箱建立BP人工神经元网络,预测SCM822H齿轮钢的性能.选择10×12×3网络结构及基于Levenberg-Marquardt 优化算法和改进的误差函数的训练函数trainbr,BP网络对SCM822H齿轮钢的性能进行快速训练的同时,使网络的泛性得到提高.最后对网络性能进行回归分析,证明了网络设计的合理性.使用训练好的网络对SCM822H齿轮钢力学性能及淬透性进行预测,预测结果表明,网络具有较高的预测精度,可在实际生产和科学研究中进行应用.     

Neural network-based EKG pattern recognition

The highly nonlinear chaotic nature of electrocardiogram (EKG) data represents a well-suited application of artificial neural networks (ANNs) for the detection of normal and abnormal heartbeats. Digitized EKG data were applied to a two-layer feed-forward neural network trained to distinguish between different types of heartbeat patterns. The Levenberg–Marquardt training algorithm was found to provide the best training results. In our study, the trained ANN correctly distinguished between normal heartbeats and premature ventricular contractions in 92% of the cases presented.     

基于支持向量回归机的公路货运量预测模型*

为了提高公路货运量预测的能力,应用基于结构风险最小化准则的标准支持向量回归机方法来研究公路货运量预测问题.在选择适当的参数和核函数的基础上,通过对成都公路货运量时间序列进行预测,并与人工神经网络、线性回归分析等方法进行了对比,发现该方法能获得最小的训练相对误差和测试相对误差.     

Adaptive resonance neural classifier for identification of gases/odours using an integrated sensor array

A new approach to intelligent gas sensor (IGS) design using a classifier based on adaptive resonance theory (ART) artificial neural network (ANN) is presented. Using published data of sensor arrays fabricated and characterised at our laboratory, we demonstrate excellent gas/odour identification performance of our classifier for a 4-gas, 4-sensor system to identify individual gas/odour. Since the ART neural network is a self-organising classifier trained in the unsupervised mode, it avoids the drawbacks associated with static feedforward neural networks trained with a locally optimal backpropagation-type training algorithms applied by researchers in the recent past. The ART neural network offers easy implementability and real time performance in addition to giving excellent classification accuracy as demonstrated by our experiments.     

气体传感器阵列测试算法研究

采用人工神经网络中的BP神经网络处理阵列式气体传感器信号,气体传感器阵列与前馈神经网络模式识别技术结合而成的人工嗅觉系统被用来进行混合气体的定量分析.利用计算机模拟方法对非线性气体传感器阵列进行模拟,并运用MATLAB神经网络工具箱设计了BP网络,最后,对模拟数据进行了比较.结果表明:神经网络法具有非线性逼近能力强、识别率较高等特点.     

Comparison of neural network configurations in the long-range forecast of southwest monsoon rainfall over India

The accurate long-range forecast of southwest rainfall can have manifold benefits for the country, from disaster mitigation and town planning to crop planning and power generation. In this paper, the rainfall has been modeled using artificial neural network (ANN) with different network configurations. Performance of these networks are compared with some results found in the literature. The networks have also been tested for the data outside the range of the trained data and compared with known results. The present network is found to be better in term of predictions than the previous results by others. Southwest monsoon rainfall over India for 6 years in advance has been predicted.