School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China.
Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China.
Sensors (Basel). 2020 Jan 5;20(1):299. doi: 10.3390/s20010299.
The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.
各种智能终端的控制效果受到数据传感精度的影响。滤波方法一直是提高传感水平的典型软计算方法。由于传统卡尔曼滤波器中实际系统的识别困难和经验参数估计，本文提出了一种基于神经元的卡尔曼滤波器。首先，设计了改进的卡尔曼滤波器的框架，其中引入了神经单元。其次，利用非线性自回归模型挖掘神经单元的功能。神经单元优化了滤波过程，以减少不切实际的系统模型和假设参数的影响。第三，提出了基于新卡尔曼滤波器的自适应滤波算法。最后，通过仿真信号和实际测量对滤波器进行了验证。结果表明，该滤波器在软计算解决方案中有效地消除了噪声。
