BASED ON RBF NEURAL NETWORK VEHICLE OPERATING POSTURE FOR RAPID DETECTION OF INTELLIGENT SENSOR CALIBRATION METHOD

2016 ◽  
Author(s):  
Xin Xu ◽  
Jie Li ◽  
Nan-Feng Zhang
Keyword(s):  
Neural Network ◽  
Rapid Detection ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Sensor Calibration ◽  
Intelligent Sensor

Agricultural Machinery Operation Posture Rapid Detection Intelligent Sensor Calibration Method Based on RBF Neural Network

2013 ◽  
Vol 373-375 ◽  
pp. 932-935 ◽  
Author(s):  
Nan Feng Zhang ◽  
Jing Feng Yang ◽  
Yue Ju Xue ◽  
Zhong Li ◽  
Xiao Lin Huang
Keyword(s):  
Neural Network ◽  
Rapid Detection ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Test Results ◽  
Agricultural Machinery ◽  
Intelligent Sensor ◽  
Status Data

Based on agricultural machinery body posture detection parameters and wheels gesture detection parameters collected by gyro inertial measurement unit, an agricultural machinery operation posture rapid detection method is proposed in this paper. The test results calibrated by RBF neural network show that, the test results of the method are accurate and available, and the method is effective and available for real-time body and wheel status data to further understand the agricultural machinery.

scholarly journals Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Li Wang ◽  
Shimin Lin ◽  
Jingfeng Yang ◽  
Nanfeng Zhang ◽  
Ji Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Traffic Congestion ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Rbf Neural Network ◽  
Calibration Method ◽  
Theory Model ◽  
Point Of View ◽  
Urban Traffic

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.

The Research on the Static Calibration of Fingertip Force Sensor for Underwater Dexterous Hand on RBF Neural Network

2007 ◽  
Vol 10-12 ◽  
pp. 267-270
Author(s):  
Peng Jia ◽  
Qing Xin Meng ◽  
Hua Wang ◽  
Hai Bo Wang
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Force Sensor ◽  
Calibration Method ◽  
Complex Task ◽  
Calibration System ◽  
Static Calibration ◽  
Fingertip Force ◽  
Calibration Experiment ◽  
Dexterous Hand

The fingertip force sensor is the key for the complex task of the dexterous underwater hand, in order to safely grasp an unknown object using the dexterous underwater hand and accurately perceive its position in the fingers, a sensor should be developed, which can detect the force and position simultaneously. Furthermore, this sensor should be used underwater. It is difficult to employ the accustomed calibration method for the characteristic of the fingertip force sensor, and the accustomed method is not able to assure the precision. A calibration method based on RBF (Radial-Basis Function) neural network is introduced. Furthermore, the calibration system and program are also designed. The calibration experiment of the sensor is carried out. The results show the nonlinear calibration method based on RBF neural network assure the precision of the sensor, which meets the demand of research on the underwater dexterous hand.

Design of Intelligent Controller for Ship Motion with Input Saturation Based on Optimized Radial Basis Function Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Renqiang Wang ◽  
Qinrong Li ◽  
Shengze Miao ◽  
Keyin Miao ◽  
Hua Deng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sliding Mode Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Ship Motion ◽  
Mode Control

Abstract: The purpose of this paper was to design an intelligent controller of ship motion based on sliding mode control with a Radial Basis Function (RBF) neural network optimized by the genetic algorithm and expansion observer. First, the improved genetic algorithm based on the distributed genetic algorithm with adaptive fitness and adaptive mutation was used to automatically optimize the RBF neural network. Then, with the compensation designed by the RBF neural network, anti-saturation control was realized. Additionally, the intelligent control algorithm was introduced by Sliding Mode Control (SMC) with the stability theory. A comparative study of sliding mode control integrated with the RBF neural network and proportional–integral–derivative control combined with the fuzzy optimization model showed that the stabilization time of the intelligent control system was 43.75% faster and the average overshoot was reduced by 52% compared with the previous two attempts. Background: It was known that the Proportional-Integral-Derivative (PID) control and self-adaptation control cannot really solve the problems of frequent disturbance from external wind and waves, as well as the problems with ship nonlinearity and input saturation. So, the previous ship motion controller should be transformed by advanced intelligent technology, on the basis of referring to the latest relevant patent design methods. Objective: An intelligent controller of ship motion was designed based on optimized Radial Basis Function Neural Network (RBFNN) in the presence of non-linearity, uncertainty, and limited input. Methods: The previous ship motion controller was remodeled based on Sliding Mode Control (SMC) with RBFNN optimized by improved genetic algorithm and expansion observer. The intelligent control algorithm integrated with genetic neural network solved the problem of system model uncertainty, limited control input, and external interference. Distributed genetic with adaptive fitness and adaptive mutation method guaranteed the adequacy of search and the global optimal convergence results, which enhanced the approximation ability of RBFNN. With the compensation designed by the optimized RBFNN, it was realized anti-saturation control. The chattering caused by external disturbance in SMC controller was reduced by the expansion observer. Results: A comparative study with RBFNN-SMC control and fuzzy-PID control, the stabilization time of the intelligent control system was 43.75% faster, the average overshoot was reduced by 52%, compared to the previous two attempts. Conclusion: The intelligent control algorithm succeed in dealing with the problems of nonlinearity, uncertainty, input saturation, and external interference. The intelligent control algorithm can be applied into research and development ship steering system, which would be created a new patent.

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