Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a dis...Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.展开更多
车体静强度试验需要通过液压缸对车体进行作用力加载。针对传统车体静强度试验台手动调节液压缸作用力操作复杂、精度低以及试验台信息化程度低、监管困难等问题,基于LabVIEW设计了车体静强度试验台测控系统。基于LabVIEW开发了该测控...车体静强度试验需要通过液压缸对车体进行作用力加载。针对传统车体静强度试验台手动调节液压缸作用力操作复杂、精度低以及试验台信息化程度低、监管困难等问题,基于LabVIEW设计了车体静强度试验台测控系统。基于LabVIEW开发了该测控系统上位机软件,在控制算法上,针对液压缸加载特性和系统安全性、鲁棒性的要求,对PID(proportion integration differentiation,比例积分微分)算法进行改进并基于自适应算法对PID参数进行调整;选择S7-200 SMART PLC(programmable logic controller,可编程逻辑控制器)作为下位机来对液压泵站进行控制,上、下位机间采用OPC(object linking and embedding for process control,过程控制中的对象链接和嵌入)技术进行通信;基于以太网开发了试验台信息化系统,并设计了信息化流程。试验结果证明:该测控系统实现了对液压缸作用力的快速、准确控制,具有较强的安全性和鲁棒性;达到了对液压泵站远程操作、数据实时采集及对试验台信息化管理的目标。该测控系统可推广至需对液压缸作用力进行自动控制和对系统信息化要求较高的实际应用中。展开更多
Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rel...Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.展开更多
基金supported by the National Natural Science Foundation of China(61890930-5,61533002,61603012)the Major Science and Technology Program for Water Pollution Control and Treatment of China(2018ZX07111005)+1 种基金the National Key Research and Development Project(2018YFC1900800-5)Beijing Municipal Education Commission Foundation(KM201710005025)
文摘Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.
文摘车体静强度试验需要通过液压缸对车体进行作用力加载。针对传统车体静强度试验台手动调节液压缸作用力操作复杂、精度低以及试验台信息化程度低、监管困难等问题,基于LabVIEW设计了车体静强度试验台测控系统。基于LabVIEW开发了该测控系统上位机软件,在控制算法上,针对液压缸加载特性和系统安全性、鲁棒性的要求,对PID(proportion integration differentiation,比例积分微分)算法进行改进并基于自适应算法对PID参数进行调整;选择S7-200 SMART PLC(programmable logic controller,可编程逻辑控制器)作为下位机来对液压泵站进行控制,上、下位机间采用OPC(object linking and embedding for process control,过程控制中的对象链接和嵌入)技术进行通信;基于以太网开发了试验台信息化系统,并设计了信息化流程。试验结果证明:该测控系统实现了对液压缸作用力的快速、准确控制,具有较强的安全性和鲁棒性;达到了对液压泵站远程操作、数据实时采集及对试验台信息化管理的目标。该测控系统可推广至需对液压缸作用力进行自动控制和对系统信息化要求较高的实际应用中。
基金supported by the National Natural Science Foundation of China (Nos. 61373137, 61572261, 61572260, and 61373017)Major Program of Jiangsu Higher Education Institutions (No. 14KJA520002)Graduate Student Research Innovation Project (Nos. KYLX16_0666 and KYLX16_0670)
文摘Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.
