高分子湿度传感器非线性误差校正的神经网络模型设计

本文介绍了湿度测量中的传感器误差问题，提出了一种基于人工神经网络的非线性滤波器模型，并成功地应用于温湿度传感器非线性误差的校正中。     

高精确度传感器非线性误差的研究

深入探讨了应变式传感器的非线性误差,并导入了传感器本身所存在的非线性误差计算公式,利用仿真技术,对贴片区进行应力计算和分析,提出了传感器非线性误差主要来源于贴片区应变梯度这一观点,并导出相应非线性误差计算公式。     

用遗传网络校正传感器非线性误差的研究

文章描述了一种用反函数校正传感器非线性误差的方法。阐述了校正原理，提出了利用BP神经网络和遗传算法相结合，拟合传感器传输特性反应函数的算法，该算法可将传感器传输特性的非线性模型，改造成为与实际物理过程相一致的不失真的线性模型，给出了一个应用实例，其结果表明，可使传感器的非线性误差有较大的减少。     

基于RBF神经网络的传感器非线性误差校正方法

介绍了利用人工神经网络进行传感器非线性误差校正的原理。提出了传感器非线性误差校正的径向基函数(RBF)神经网络方法,并与采用BP神经网络校正非线性误差进行了比较。最后给出了一个仿真实验,实验结果表明:采用RBF神经网络可以明显提高网络收敛速度,大大减小传感器非线性误差,校正效果优于BP神经网络。     

传感器模糊神经网络非线性误差补偿的研究

在测量系统中。传感器的非线性特性是测量系统误差的主要来源。要提高测量系统的精度，就必须进行误差补偿。设计了一个用模糊小脑神经网络实现的补偿环节。该补偿环节是一个用神经网络拟合的传感器逆特性，通过传感器的逆特性将传感器非线性特性改造成与实际物理过程相一致的不失真的线性特性。从而减小非线性误差。通过应用实验，验证了该方法的有效性。     

传感器信号的非线性补偿

针对传感器信号中存在的非线性误差 ,介绍了硬件补偿和软件补偿两种方法。硬件补偿是基于电压源可变来实现传感器特性的线性化 ,从而消除了非线性误差 ;软件补偿是通过函数拟合的办法来有效减小信号中的非线性误差 ,进而提高传感器的准确度。     

基于三次多项式的磁弹性传感器非线性误差修正

磁弹性传感器输出信号大,具有较高的过载能力,非常适合用于测量金属板材的轧制力和桥梁支撑柱的压力。理论上,传感器输入和输出之间是线性关系;但实际上,传感器的输入输出特性都是非线性的。非线性误差是传感器的关键指标,直接决定产品的定级。对于重复性好和回差小的传感器,可以用三次多项式修正降低其非线性误差,以提高产品等级。通过400 t磁弹性传感器在标准测力机上进行的测力试验,验证了非线性误差修正方法的可行性,将传感器的非线性误差从2.75%减小到了0.35%,大大降低了非线性误差,且修正方法简单实用,使用单片机的仪表在现场即可进行数据修正操作。对于电阻应变式传感器和其他线性仪表,此方法同样适用。     

称重传感器非线性误差自适应补偿方法

额定量程内称重传感器的非线性误差不同,为此阐述了称重传感器的非线性误差特性,提出了一种非线性误差自适应分段补偿方法:在额定量程的上限区,采用基于径向基函数神经网络(RBFNN)的补偿网络完成传感器非线性误差补偿;在下限区,采用数字滤波器完成非线性误差补偿;在中间区,传感器不补偿。同时利用自适应选择网络,完成了分段补偿的选择。实验表明,采用这种方法补偿后的称重传感器下限区、中间区与上限区的最大相对误差分别由补偿前的0.2、0.4、1.37下降到0.16、0.04、0.07,补偿效果明显。     

高精度传感器弹性元件应变梯度的非线性误差分析

定量地分析了弹性元件应变梯度产生的非线性误差，通过分析计算表明弹性元件粘贴区的应变梯度是应变式高精度传感器非线性误差产生的主要因素之一。     

传感器静态校准曲线的最佳拟合

传感器的基本性能指标之一是其非线性误差,本文在保证其非线性误差最小的条件下,推导了传感器静态校准曲线的最佳拟合数学模型。     

Dynamic adaptation model and equal‐whiteness CCT curves for the choice of display reference white

Abstract— A dynamic adaptation model (DAM), based on the variations in the luminance levels under the same chromaticity viewing conditions and equal‐whiteness correlated‐color‐temperature (CCT) curves (EWCCs) derived by using the proposed DAM, is proposed. The proposed model was obtained by the transformation of the test colors for high luminance into the corresponding colors for low luminance. In the proposed model, the optimal coefficients are obtained from the corresponding color data from Breneman's experiments. From the experimental results, it was confirmed that the chromaticity errors between the predicted colors by the proposed model and the corresponding colors of Breneman's experiments are 0.004 in u′v′ chromaticity coordinates. The prediction performance of the proposed model is excellent because this error is the threshold value that distinguishes two adjacent color patches. Equal‐whiteness CCT curves (EWCs) are also proposed using the proposed DAM. By using the proposed EWCCs, an analysis of the difference between the selected video‐display‐unit reference‐white CCT values can be made. Additionally, the proposed EWCCs can be used as the theoretical standard which determines the reference white of the color video display units.     

Adaptive skin color modeling using the skin locus for selecting training pixels

Techniques for color-based tracking of faces or hands often assume a static skin model yet skin color, as measured by a camera, can change when lighting changes. Therefore, for robust skin pixel detection, an adaptive skin color model must be employed. We demonstrate a chromaticity-based constraint to select training pixels in a scene for updating a dynamic skin color model under changing illumination conditions. The method makes use of the ‘skin locus’ of a camera, that is, the area in chromaticity space where skin chromaticity under various lighting and camera calibration conditions is observed. Skin color models derived from the technique are compared with that derived by a common spatial constraint and is shown to be more consistent with manually extracted ground truth skin model per frame even as localization errors increase. The technique is applied to color-based face tracking in indoor and outdoor videos and is shown to succeed more often than other color model adaptation techniques.     

基于NSST的改进双通道PCNN多聚焦RGB图像融合

针对光学成像设备景深有限、图像部分失焦的问题,提出一种基于非下剪切波变换(NSST)的改进双通道脉冲神经耦合网络(PCNN)融合算法。首先,该算法采用Lab颜色空间分割RGB图像的亮度分量和色度分量间的关联性得到亮度和色度通道子图;然后,亮度通道子图使用NSST重构,色度通道子图使用能量匹配融合;针对融合时阈值设置和点火量化产生的误差,提出改进双通道PCNN模型融合,并结合对比敏感度函数(CSF)自适应设定PCNN参数;最后,亮度和色度重构图通过逆Lab得到最终融合图。实验结果证实,该算法可有效减小失真,抑制伪影并保留边缘轮廓信息,提升全场景清晰度。     

Statistical Modeling of Colour Data

In this paper we investigate how best to model naturally arising distributions of colour camera data. It has become standard to model single mode distributions of colour data by ignoring the intensity component and constructing a Gaussian model of the chromaticity. This approach is appealing, because the intensity of data can change arbitrarily due to shadowing and shading, whereas the chromaticity is more robust to these effects. However, it is unclear how best to construct such a model, since there are many domains in which the chromaticity can be represented. Furthermore, the applicability of this kind of model is questionable in all but the most basic lighting environments.We begin with a review of the reflection processes that give rise to distributions of colour data. Several candidate models are then presented; some are from the existing literature and some are novel. Properties of the different models are compared analytically and the models are empirically compared within a region tracking application over two separate sets of data. Results show that chromaticity based models perform well in constrained environments where the physical model upon which they are based applies. It is further found that models based on spherical representations of the chromaticity data provide better performance than those based on more common planar representations, such as the chromaticity plane or the normalised colour space. In less constrained environments, however, such as daylight, chromaticity based models do not perform well, because of the effects of additional illumination components, which violate the physical model upon which they are based.     

Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks

This paper investigates the development and experimental implementation of an adaptive dynamic nonlinear model inversion control law for a Twin Rotor MIMO System (TRMS) using artificial neural networks. The TRMS is a highly nonlinear aerodynamic test rig with complex cross-coupled dynamics and therefore represents the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. The proposed on-line learning algorithm updates the weights and biases of the neural network using the error between the set-point and the real output. The real-time response of the method shows a satisfactory tracking performance in the presence of inversion errors caused by model uncertainty. The approach is therefore deemed to be suitable to apply real-time to other nonlinear systems with necessary modifications.     

Robust model-order reduction of complex biological processes

This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation (PDE) model of a complex biological process. Based on a nonlinear, distributed parameter model of the same process which was validated against experimental data of an existing, pilot-scale BNR activated sludge plant, we developed a state-space model with 154 state variables in this work. A general algorithm for robustly reducing the nonlinear PDE model is presented and based on an investigation of five state-of-the-art model-order reduction techniques, we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors. The Singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications.     

舰船水压信号的预测方法研究

提出了一种能从海浪水压信号背景下提取舰船水压信号的预测异常检测(PAD)法。模型预测值与测量值相比较所得的差值被作为检测舰船水压信号是否存在的判据。讨论了作为PAD中预测模型的线性的自回归(AR)模型和非线性的神经网络(NN)模型，并用模拟数据和实测数据对二者进行了比较。仿真结果表明，PAD效果良好，预测模型中，NN模型要优于AR模型。     

LANDSAT test of diffuse reflectance models for aquatic suspended solids measurement

LANDSAT radiance data were used to test mathematical models relating diffuse reflectance to aquatic suspended solids concentration. Digital CCT data for LANDSAT passes over the Bay of Fundy, Nova Scotia were analyzed on a General Electric Co. Image 100 multispectral analysis system. Three data sets were studied separately and together in all combinations with and without solar angle correction. Statistical analysis and chromaticity analysis show that a nonlinear relationship between LANDSAT radiance and suspended solids concentration is better at curve-fitting than a linear relationship. In particular, the quasi-single-scattering diffuse reflectance model developed by Gordon and coworkers is corroborated. The Gordon model applied to 33 points of MSS 5 data combined from three dates produced r = 0.98.     

基于不确定模型误差系统的变分贝叶斯STCKF

强跟踪容积卡尔曼滤波器在对含有模型误差和时变噪声的非线性系统进行滤波时, 容易出现性能降低甚至发散. 鉴于此, 提出一种基于变分贝叶斯的强跟踪容积卡尔曼滤波算法. 该算法运用虚拟噪声法补偿模型误差, 假设虚拟噪声均值非零, 且满足高斯分布, 虚拟噪声方差服从逆gamma分布, 在强跟踪容积卡尔曼滤波器估计状态的同时, 采用变分贝叶斯推理估计虚拟噪声参数. 仿真结果表明, 所提出算法对含模型误差与时变噪声的非线性系统具有较好的估计精度, 相比于自适应算法具有更强的鲁棒性.     

基于改进型非线性干扰观测器的爬壁机器人轨迹跟踪

针对爬壁机器人建模不准确及容易受外部扰动的影响造成位置及姿态误差的问题,提出了一种基于改进型非线性干扰观测器的轨迹跟踪控制方案.首先通过反演控制设计了一个运动学控制器为机器人动力学控制提供参考质心速度与角速度.其次应用改进型非线性扰动观测器作为前馈控制对建模误差及外部扰动进行估计,并保证扰动误差以指数形式收敛.最后针对引入干扰观测器的动力学模型设计了滑模控制器.该方案对外界干扰进行了快速补偿,并通过Lyapunov定理证明了其稳定性.仿真结果表明该控制方法对于克服建模误差及外界干扰具有较好的效果.