无人车的室内自主驾驶中常用到EKF-SLAM（Simultaneous Localization and Mapping）技术。单纯的编码器SLAM技术，由于其长时间的运行会导致累计误差过大，使得定位非常不准确，所以需要一种技术，对位置信息的定位方式加以辅助，考虑...无人车的室内自主驾驶中常用到EKF-SLAM（Simultaneous Localization and Mapping）技术。单纯的编码器SLAM技术，由于其长时间的运行会导致累计误差过大，使得定位非常不准确，所以需要一种技术，对位置信息的定位方式加以辅助，考虑到二维码识别技术的方便性以及易用性，本文采用二维码人工路标作为绝对定位方式的标签，提升EKF—SLAM的定位准度，并利用扩展卡尔曼滤波进行多数据融合，通过实验验证了实验该方案的可行性与实用性。展开更多
Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. Th...Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.展开更多
文摘无人车的室内自主驾驶中常用到EKF-SLAM（Simultaneous Localization and Mapping）技术。单纯的编码器SLAM技术，由于其长时间的运行会导致累计误差过大，使得定位非常不准确，所以需要一种技术，对位置信息的定位方式加以辅助，考虑到二维码识别技术的方便性以及易用性，本文采用二维码人工路标作为绝对定位方式的标签，提升EKF—SLAM的定位准度，并利用扩展卡尔曼滤波进行多数据融合，通过实验验证了实验该方案的可行性与实用性。
文摘Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.
