基于神经网络辨识的遥康复系统变增益控制

针对康复训练过程中患者肌肉痉挛会对力反馈遥操作系统稳定性和从机械手速度平滑性产生较大影响的问题,提出了一种新的基于反向传播(BP)神经网络辨识的变增益控制方法。该方法通过 BP 神经网络实时辨识患肢动力学参数的变化并进行自适应调整控制增益,不仅消除了因患者肌肉痉挛带来的不稳定性,而且减少了其对系统运动平滑性的影响,可提高康复训练效果和起到抑制患者痉挛状态的作用。分析和仿真试验结果表明,该控制方法与传统的控制方法相比,可有效地抑制患者因肌肉痉挛带来的干扰并具有较好的稳定性和平滑性。

基于神经网络辨识的变增益控制在遥康复中的应用

在患者进行遥康复训练时,肌肉痉挛会对遥康复训练系统稳定性及从机械手速度平滑性产生较大的影响。针对这些问题提出了一种新的基于BP神经网络辨识的变增益控制方法。通过BP神经网络实时辨识患肢动力学参数并根据其变化进行自适应调整控制增益,不仅消除了因患者肌肉痉挛带来的不稳定性,而且减少了其对系统运动平滑性的影响,可提高康复训练效果。分析和仿真试验结果表明,该控制方法与传统的控制方法相比,可有效地抑制患者因肌肉痉挛带来的干扰并具有较好的稳定性和平滑性。

生物滴滤塔废气处理过程的神经网络建模

在生物滴滤塔处理有机废气过程中,参数间存在比较明显的耦合,同时非线性性能也较强,精确数学模型的建立比较困难。采用辨识方法,建立BP神经网络模型模拟处理生物滴滤塔有机废气的过程。同时采用Levenberg-Marquardt算法解决标准BP训练算法训练速度慢的问题。通过遗传算法优化BP网络的初始权值,避免网络训练时陷入局部极小点,最终的网络模型能够较好得模拟实际对象的运行过程。

A back-propagation neural-network-based displacement back analysis for the identification of the geomechanical parameters of the Yonglang landslide in China

Xigeda formation is a type of hundredmeter-thick lacustrine sediments of being prone to triggering landslides along the trunk channel and tributaries of the upper Yangtze River in China. The Yonglang landslide located near Yonglang Town of Dechang County in Sichuan Province of China, which was a typical Xigeda formation landslide, was stabilized by anti-slide piles. Loading tests on a loading-test pile were conducted to measure the displacements and moments. The uncertainty of the tested geomechanical parameters of the Yonglang landslide over certain ranges would be problematic during the evaluation of the landslide. Thus, uniform design was introduced in the experimental design,and by which, numerical analyses of the loading-test pile were performed using Fast Lagrangian Analysis of Continua(FLAC3D) to acquire a database of the geomechanical parameters of the Yonglang landslide and the corresponding displacements of the loadingtest pile. A three-layer back-propagation neural network was established and trained with the database, and then tested and verified for its accuracy and reliability in numerical simulations. Displacement back analysis was conducted by substituting the displacements of the loading-test pile to the well-trained three-layer back-propagation neural network so as to identify the geomechanical parameters of the Yonglang landslide. The neuralnetwork-based displacement back analysis method with the proposed methodology is verified to be accurate and reliable for the identification of the uncertain geomechanical parameters of landslides.