基于LS-SVM的传感器智能校正及温度补偿

提出一种基于最小二乘支持向量机(LS-SVM)的传感器非线性校正及温度补偿的新方法,并给出了相应的过程和算法。在该方法中,LS-SVM被用作构建逆模型,并通过该模型映射传感器非线性特性,同时实现了传感器的温度补偿和非线性校正。通过实际电容式压力传感器校正的实验结果表明:所提模型建模速度比SVM模型高1~2个数量级,补偿误差仅为SVM模型的20%左右。因此,该学习速度快、补偿精度高、抗噪声干扰能力强,适合传感器温度补偿及校正。     

基于神经网络的多传感器系统误差校正方法

为提高传感器的准确度，减少传感器的研制成本，提出了基于神经网络多传感器误差补偿的方法。该方法利用BP网络较强的非线性映射能力，网络通过学习能实现对传感器系统误差的补偿。通过实例及仿真证明了该方法的有效性。     

基于小波神经网络的电涡流传感器非线性补偿

为消除电涡流传感器的非线性误差,提高其测量精度,提出了一种基于小波神经网络和遗传算法的电涡流传感器非线性补偿方法。该方法利用小波神经网络的非线性映射能力,使得传感器的输入与输出线性化,并使用遗传算法搜寻网络的最优初始值,加强网络的非线性逼近能力和收敛能力,显著提高电涡流传感器的非线性补偿效果。实验结果表明,经过补偿后,极大提高了传感器的精度,传感器输出电压最大绝对误差为15.55mV,最大相对误差为1.36%,非线性误差为0.34%。     

基于RBF神经网络对电磁力平衡传感器测量精度的研究

本文介绍一种利用径向基函数（RBF）神经网络改善传感器精度的新方法。RBF网络具有良好的非线性映射能力,能以任意精度逼近任何非线性函数。本文利用RBF网络较强的非线性映射能力对电磁力平衡传感器进行温度补偿．实验表明该方法实现了传感器高精度的温度补偿。     

基于导数约束的称重传感器非线性误差补偿方法

电阻应变式称重传感器存在严重的非线性误差,直接影响称重结果的准确度。本文首先阐述了称重传感器的非线性误差机理与误差补偿原理,提出了一种基于导数约束的称重传感器非线性误差补偿方法。该方法根据称重传感器输入-输出特性曲线的单调递增性,构造神经网络补偿模型训练的约束条件,完成神经网络优化设计,弥补了因训练样本不足导致的网络泛化误差大的缺陷,同时讨论了惩罚因子对网络性能的影响。实验表明,采用这种基于导数约束神经网络补偿方法（DCNN方法）的称重传感器的非线性误差远小于补偿前的误差;同时当训练样本不足时,DCNN方法比传统训练方法（仅利用数据样本训练神经网络,DINN）具有更好的泛化能力,称重准确度更高。     

一种用RBF神经网络改善传感器测量精度的新方法

介绍一种利用径向基函数(RBF)神经网络和智能温度传感器DS18B20改善传感器精度的新方法。RBF网络具有良好的非线性映射能力、自学习和泛化能力,通过大量的样本数据训练构建了双输入单输出网络模型,采用改进的算法实现了传感器高精度温度补偿。     

具有动态补偿能力的神经网络模型及其在动态系统建模中的应用

本文在多层前馈神经网络模型基础上，引入误差动态反馈环节，从而形成一种新的具有动态补偿能力的神经网络模型。新模型的训练利用反向传播原理实现。采用该模型对非线性动态系统进行建模时，能显著提高建模精度，特别是在网络模型工作时，对新出现的输出误差具有动态补偿能力，文中给出了新网络模型的结构和学习算法，最后是仿真实例。     

基于ANN逆模型补偿的复合控制系统仿真

为对复杂非线性系统进行辨识建模和实施有效控制,分析了基于神经网络的非线性系统逆模型的辨识和控制原理,研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想,时常规PID控制器＋前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明,基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构,可提高逆模型的泛化能力和非线性系统的控制精度。     

递归网络模型在传感器动态补偿中的应用

为改善传感器的动态响应特性 ,对其输出结果进行动态补偿是一个有效方法。本文介绍了传感器动态补偿的原理 ,基于递归网络模型的良好的动态映射能力 ,探讨了应用该模型进行传感器动态补偿的实现方法 ,实验结果表明补偿后传感器具有理想的输入输出特性 ,从而验证了该方法的可行性和有效性。     

基于神经网络的传感器动态补偿算法及DSP实现

为了改善传感器的动态响应特性,对其输出结果进行动态补偿是一个有效方法;讨论了基于自适应神经网络的传感器动态逆建模方法,采用网络分块训练和可变学习因子的方法来提高训练的精度,缩短收敛时间;研究了在加入不同信噪比的随机噪声下应用该模型实现传感器动态补偿的可行性;对典型的压电传感器模型进行了仿真,仿真结果表明补偿后传感器模型的响应速度加快,同时还可以抑制噪声;研制了基于数字信号处理器的数据采集及补偿系统并运用该系统对传感器模拟器输出的数据进行了采集,试验结果表明该系统能够准确的采集存储数据,同时还能够修正由传感器模拟器引起的动态误差。     

Enhancement of the neural network modeling accuracy using a submodeling decomposition-based technique, application in gas sensor

This paper presents an algorithm based on the use of artificial neural networks (ANNs) in order to reduce the processing time and to improve the accuracy in ANN modeling, which can be accomplished with a division of the model to submodels by input subintervals. We apply this method with a gas sensor aiming to accurately control the small gas leaks, thus decreasing the risk of false alarms and missed detections. The sensor model accurately, especially in small concentrations, expresses the nonlinear character of the response and the dependence on temperature and relative humidity in addition to the gas nature dependency. The corrector linearizes and compensates the sensor’s responses. The results obtained show the effectiveness of the proposed technique.     

基于DRNN的传感器的建模研究

针对传感器受温度影响的复杂非线性输入输出特性,利用对角递归神经网络（DRNN）建模,并实现了温度补偿和非线性校正。对于权值的训练采用LM算法,克服了BP算法收敛慢的缺陷,使其在保证收敛的前提下,提高了收敛速度。实验表明：应用DRNN对传感器建模是一种行之有效的方法。     

基于加权支持向量机的移动建模方法及其在软测量中的应用

工业过程软测量技术的核心问题是建立软测量模型,然而,利用传统全局建模方法与多 模型建模方法进行复杂工业过程软测量建模时,在不同程度上存在一些问题.本文利用支持向量 机(SVMs)泛化能力强的特点,结合局部加权学习(LWL)算法思想,提出一种适于局部学习的加 权支持向量机(W_SVMs)学习算法和基于这种算法的移动建模方法.利用这种建模方法对Box- Jenkins煤气炉和重油催化裂化(FCCU)装置进行分析建模,并与其它不同建模方法进行比较,显 示了该方法的优点和有效性.     

传感器网络中基于卡尔曼滤波的能量高效Top—k查询处理技术

无线传感器网络能量受限,如何实现top-k查询处理的能量高效从而延长网络的生命周期是该领域研究的一个重要课题。论文利用传感器节点读数的时空相关性,提出运用卡尔曼滤波根据已知节点读数对未知节点读数估计的时空建模方法,进而提出基于预测机制的区域采样方法（RegionSampling,RS）。实验表明,论文提出的查询方法不但可以满足用户的查询精度要求,而且大大减少了传感器网络的通信次数节省了能量,从而延长了网络的生命周期。     

基于统计回归的质量推断方法

统计回归是软测量建模的一个重要手段.本文在回顾了典型统计回归方法的基础上,采用部分最小二乘(PLS)建立高定量造纸过程的水分软测量模型.鉴于软测量模型的扰动拟合本质,提出了基于扰动估计的软测量建模策略.水分软测量模型的建立,验证了该建模策略的有效性.     

A modular design of Bayesian networks using expert knowledge: Context-aware home service robot

Recently, demand for service robots increases, and, particularly, one for personal service robots, which requires robot intelligence, will be expected to increase more. Accordingly, studies on intelligent robots are spreading all over the world. In this situation, we attempt to realize context-awareness for home robot while previous robot research focused on image processing, control and low-level context recognition. This paper uses probabilistic modeling for service robots to provide users with high-level context-aware services required in home environment, and proposes a systematic modeling approach for modeling a number of Bayesian networks. The proposed approach supplements uncertain sensor input using Bayesian network modeling and enhances the efficiency in modeling and reasoning processes using modular design based on domain knowledge. We verify the proposed method is useful as measuring the performance of context-aware module and conducting subjective test.     

霍尔式微位移传感器建模的实现方法

为了正确反映霍尔式微位移传感器的特性,本文首先介绍霍尔式微位移传感器的工作原理,得出霍尔式微位移传感器被测试件位移量与相关测量电路输出电压(S,V)关系特征,然后基于最小二乘估计算法基本原理,提出了运用MATLAB语言建立霍尔式微位移传感器(S,V)关系特征的数学模型的方法,给出建模的程序流程图以及仿真结果。     

Feature extraction and selection based on vibration spectrum with application to estimating the load parameters of ball mill in grinding process

Feature extraction and selection are important issues in soft sensing and complex nonlinear system modeling. In this paper, a new feature extraction and selection approach based on the vibration frequency spectrum is proposed to estimate the load parameters of wet ball mill in grinding process. This approach can simplify the modeling process. In this study, the vibration acceleration signals are first transformed into the frequency spectrum by fast Fourier transform (FFT). Then the candidate features are extracted and selected from the frequency spectrum, which include characteristic frequency sub-bands, spectral principal components, and features of local peaks. Mutual information, spectral segment clustering and kernel principal component analysis are used to obtain these candidate features. Finally, a combinatorial optimization method based on adaptive genetic algorithm selects the input sub-set and parameters of the soft sensor model simultaneously. This approach is successfully applied in a laboratory scale wet ball mill. The test results show that the proposed approach is effective for modeling the parameters of mill load.     

A novel calibration approach of soft sensor based on multirate data fusion technology

In multirate chemical processes, soft sensor is commonly used for fast-rate quality estimations. As the kernel of soft sensor, modeling technique has drawn much attention from researchers. But most of the modeling methods have disadvantages of uncertain estimation errors and limited modeling capability. To these issues, soft sensor calibration is considered as an efficient alternative, which however is often ignored by researchers and the current application level is relatively low. A novel soft sensor calibration method is proposed in this paper. Under multirate sampling conditions, data fusion technology based on Kalman filter is introduced into soft sensor maintenance to integrate the soft sensor model estimations with process measurements. The performance of the algorithm is evaluated through simulations and a laboratory scale experiment. The factors that may influence the performance are also discussed in detail. The results demonstrate that the multirate Kalman filter approach is able to improve the accuracy and reliability of soft estimations, when the essential dynamics is included in the Kalman filtering model and the filter parameters are properly tuned.     

Improved occupancy grid mapping in specular environment

This paper addresses the improved method for sonar sensor modeling which reduces the specular reflection uncertainty in the occupancy grid. Such uncertainty reduction is often required in the occupancy grid mapping where the false sensory information can lead to poor performance. Here, a novel algorithm is proposed which is capable of discarding the unreliable sonar sensor information generated due to specular reflection. Further, the inconsistency estimation in sonar measurement has been evaluated and eliminated by fuzzy rules based model. To achieve the grid map with improved accuracy, the sonar information is further updated by using a Bayesian approach. In this paper the approach is experimented for the office environment and the model is used for grid mapping. The experimental results show 6.6% improvement in the global grid map and it is also found that the proposed approach is consuming nearly 16.5% less computation time as compared to the conventional approach of occupancy grid mapping for the indoor environments.