人工神经网络在工业机器人中的应用

Artificial neural networks (ANNs) have been widely used to solve a number of problems to which analytical solutions are difficult to obtain using traditional mathematical approaches. Such problems exist also in the analysis of industrial robots. This paper presents an overview of ANN applications to robot kinematics, dynamics, control, trajectory and path planning, and sensing. Reasons for using or not using ANNs to industrial robots are explained as well.     

心电图检测与辨识的人工神经网络方法

应用人工神经网络（Artificial Neural Network，ANN）算法对MIT-BIH心电数据库中的数据进行检测，对波形辨识算法做初步研究。设计三层神经网络结构：输入层、隐含层和输出层。从心电信号中提取4项特征参数作为输入层的输入量，并对MIT-BIH心电数据库中的15例心电数据进行了检测。表明该算法对QRS波总体检测灵敏度为98．96％，检测真阳性率为99．93％，对室性异位博动检测灵敏度为94．97％，检测真阳性率为98．72％。实验证实该神经几乎络算法对心电波形辨识非常有效。     

膨胀土中的吸力预测研究

膨胀土的水分特征曲线通常是在实验室测得的, 在现场测量膨胀土的吸力不仅费时而且也非常困难. 本文采用人工神经网络技术用现场测得的含水量来预测土的吸力. 网络训练首先采用水分特征曲线相应的试验数据进行监督训练, 然后利用监督训练得到的网络单元的连接权值对现场测得含水量数据进行吸力预测, 预测结果与实测结果相近, 同时并对预测结果进行了分析讨论.     

基于人工神经网络的等离子熔积层形貌试验研究

1IntroductionPlasma deposition manufacturing(PDM)is a newlydeveloped direct metal fabrication process based onplasma transferred arc surfacing[1],as shown in Fig.1.Unlike most existing rapid prototyping techn-ologies,this technique is characterized by sup…     

Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison

Visible and near infrared spectroscopy is a non-destructive, green, and rapid technology that can be utilized to estimate the components of interest without conditioning it, as compared with classical analytical methods. The objective of this paper is to compare the performance of artificial neural network (ANN) (a nonlinear model) and principal component regression (PCR) (a linear model) based on visible and shortwave near infrared (VIS-SWNIR) (400–1000 nm) spectra in the non-destructive soluble solids content measurement of an apple. First, we used multiplicative scattering correction to pre-process the spectral data. Second, PCR was applied to estimate the optimal number of input variables. Third, the input variables with an optimal amount were used as the inputs of both multiple linear regression and ANN models. The initial weights and the number of hidden neurons were adjusted to optimize the performance of ANN. Findings suggest that the predictive performance of ANN with two hidden neurons outperforms that of PCR.     

人工神经网络在中长期电力负荷预测中的应用

设计了一个由输入层、隐含层和输出层组成的三层BP网络模型,利用神经网络高度非线性建模能力,进行电力系统的中长期负荷预测.选取影响电力负荷的一些经济因素作为神经网络的输入变量,并对分别采用单个因素和多个因素的组合作为输入量对预测精度的影响进行了探讨.在多因素组合时对输入量进行了归一化处理.仿真结果证明,使用人工神经网络方法进行中长期电力负荷预测是可行和有效的.