A kind of new dynamic modeling method based on improved genetic wavelet neural networks for the robot wrist force sensor

2011 ◽  
Author(s):  
Yu A-long
Keyword(s):  
Neural Networks ◽  
Dynamic Modeling ◽  
Force Sensor ◽  
Modeling Method ◽  
Wavelet Neural Networks ◽  
Robot Wrist

The Robot Wrist Sensor Dynamic Mode Building Method Based on Genetic Wavelet Neural Networks

2013 ◽  
Vol 455 ◽  
pp. 389-394
Author(s):  
A Long Yu ◽  
Jin Qiao Dai
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Dynamic Model ◽  
Force Sensor ◽  
Dynamic Mode ◽  
Improved Genetic Algorithm ◽  
Wavelet Neural Networks ◽  
Robot Wrist ◽  
New Type ◽  
Set Up

A kind of new dynamic modeling method is presented based on improved genetic algorithm (IGA) and wavelet neural networks (WNN) and the principle of algorithm is introduced for a new type robot wrist force sensor. The dynamic model of the wrist force sensor is set up according to data of the dynamic calibration, where the structure and parameters of wavelet neural networks of the dynamic model are optimized by genetic algorithm. The results show that the proposed method can overcome the shortcomings of easy convergence to the local minimum points of BP algorithm, and the network complexity, the convergence and the generalization ability are well compromised and the training speed and precision of model are increased.

A Novel Modeling Method for Aircraft Engine Using Nonlinear Autoregressive Exogenous (NARX) Models Based on Wavelet Neural Networks

2018 ◽  
Vol 35 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Bing Yu ◽  
Wenjun Shu ◽  
Can Cao
Keyword(s):  
Neural Networks ◽  
Time Series Data ◽  
Aircraft Engine ◽  
Modeling Method ◽  
Series Data ◽  
Data Sets ◽  
Wavelet Neural Networks ◽  
Component Level ◽  
Narx Models ◽  
Nonlinear Autoregressive

Abstract A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine’s dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.

Sign in / Sign up

Export Citation Format

Share Document