Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

The robot dynamic model is often rarely known due to various uncertainties such as parametric uncertainties or modeling errors existing in complex environments. It is a key problem to find the relationship between the changes of neural network structure and the changes of input and output environments and their mutual influences. Firstly, this paper defined the conceptions of neural network solution, neural network eigen solution, neural network complete solution, and neural network partial solution and the conceptions of input environments, output environments, and macrostructure of neural networks. Secondly, an eigen solution theory of general neural networks was proposed and proven including consistent approximation theorem, eigen solution existence theorem, consistency theorem of complete solution, the partial solution, and none solution theorem of neural networks. Lastly, to verify the eigen solution theory of neural networks, the proposed theory was applied to a novel prediction and analysis model of controller parameters of grinding robot in complex environments with deep neural networks and then build prediction model with deep learning neural networks for controller parameters of grinding robot. The morphological subfeature graph with multimoment was constructed to describe the block surface morphology using rugosity, standard deviation, skewness, and kurtosis. The results of theoretical analysis and experimental test show that the output traits have an optional effect with joint action. When the input features functioning in prediction increase, higher predicted accuracy can be obtained. And when the output traits involving in prediction increase, more output traits can be predicted. The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. Compared with the traditional prediction model, the proposed model can predict output features simultaneously and is more stable.

Section: Prediction Model Of Controller Parameters Of Grindingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Tang

et al. 2019

“…Cam-shift algorithm is the commonly used gesture tracking algorithm, which is good for tracking solid objects in a black-and-white background. However, the contrast between the background color and the target is not obvious, and the tracking effect is poor [15][16][17][38][39][40]. To verify the effectiveness of the switching federated filter algorithm, a gesture tracking and positioning experiment based on the 3D interactive software is presented.…”

Section: Gesture Tracking and Positioning Experiments Based On D Imentioning

confidence: 99%

Large‐Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

Hou

2018

Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated filtering algorithm work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction.

“…All of these algorithms have their own advantages and disadvantages and are proposed for specific noise. In practice, a variety of noise generation methods have demonstrated that a single filtering algorithm cannot be effective for all types of image filtering [1][2][3][4][5][6]. In the case of the filter system based on Kinect sensor depth image, light intensity and sensor temperature are the main factors that generate the noise in the image.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of the filter system based on Kinect sensor depth image, light intensity and sensor temperature are the main factors that generate the noise in the image. In addition, the short stay at the time of image formation and the interference of the channel during image transmission are also the main reasons for the noise generation [3]. Among these noises, Gaussian noise and the saltand-pepper noise are the two most important types of noise.…”

Section: Introductionmentioning

confidence: 99%

Image Noise Preprocessing of Interactive Projection System Based on Switching Filtering Scheme

2018

The interactive projection systems based on deep images are usually disturbed by the mixed noise. Generally, several filtering methods are used in combination to resolve this problem. Although the hybrid filter can guarantee the accuracy of the image, but the algorithm is complex and time-consuming, which affects the real-time performance of the interactive projection system. In this paper, the switching system method is introduced into the filter for the first time, and an arbitrary switching filter algorithm is proposed and applied to the depth image filtering system based on Kinect sensor. The experimental results demonstrate and validate that the proposed switching filter algorithm not only effectively removes the noise but also ensures the real-time performance of tracking and achieves good target tracking performance, which makes it applicable in various image filtering processing systems.