Reinforcement adaptive learning neural network based friction compensation for high speed and precision

2002 ◽  
Author(s):  
Y.H. Kim ◽  
F.L. Lewis
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
High Speed ◽  
Friction Compensation

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

Prediction Model of Twin-Arc High Speed Submerged Arc Weld Shape Based on Improved BP Neural Network

2011 ◽  
Vol 216 ◽  
pp. 194-199 ◽  
Author(s):  
Kuan Fang He ◽  
Ji Gang Wu ◽  
Xue Jun Li ◽  
H. Long
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Penetration Depth ◽  
Adaptive Learning ◽  
Arc Welding ◽  
High Speed ◽  
Welding Process ◽  
Submerged Arc Welding ◽  
Forming Quality ◽  
Submerged Arc

Twin-Arc high-speed submerged arc welding forming quality prediction model was developed by three layers BP (Back Propagation) neural network. In the model, twin arc current, twin arc voltage, welding speed and wire spacing are selected for the study factor, weld pool width and penetration depth are weld forming quality indicators. The adaptive learning rate and additional momentum term are introduced to improve BP algorithm. Experiments show that the network structure is reasonable of the nodes by inputting and outputting layers of 6 and 2 respectively, hidden layer nodes are 13. The developed neural network model can predict the weld geometry with high computing and predictive accuracy of maximum predictive validation error of weld width and penetration depth within 9.6%, 10.3% respectively, which can be used for real-time monitoring of the quality of welding in the twin arc high-speed submerged arc welding process.

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

Erratum: High speed opto-electronic neural network

1990 ◽  
Vol 26 (20) ◽  
pp. 1739
Author(s):  
N.M. Barnes ◽  
P. Healey ◽  
P. McKee ◽  
A.W. O'Neill ◽  
M.A.Z. Rejmangreene ◽  
...  
Keyword(s):  
Neural Network ◽  
High Speed

scholarly journals A new evaluation and prediction model of sound quality of high-speed permanent magnet motor based on genetic algorithm-radial basis function artificial neural network

2021 ◽  
Vol 104 (3) ◽  
pp. 003685042110311
Author(s):  
Kai Hu ◽  
Guangming Zhang ◽  
Wenyi Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Basis Function ◽  
High Speed ◽  
Objective Evaluation ◽  
Sound Quality ◽  
Average Error ◽  
Permanent Magnet Motor ◽  
Predicted Model

Sound quality (SQ) has become an important index to measure the competitiveness of motor products. To better evaluate and optimize SQ, a novelty SQ evaluation and prediction model of high-speed permanent magnet motor (HSPMM) with better accuracy is presented in this research. Six psychoacoustic parameters of A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, fluctuation strength (FS), and perferred-frequency speech interference (PSIL) were adopted to objectively evaluate the SQ of HSPMM under multiple operating conditions and subjective evaluation was also conducted by the combination of semantic subdivision method and grade scoring method. The evaluation results show that the SQ is poor, which will have a certain impact on human psychology and physiology. The correlation between the objective evaluation parameters and the subjective scores is analyzed by coupling the subjective and objective evaluation results. The average error of multiple linear regression (MLR) model is 7.10%. It has good accuracy, but poor stability. In order to improve prediction accuracy, a new predicted model of radial basis function (RBF) artificial neural network was put forward based on genetic algorithm (GA) optimization. Compared with MLR, its average error rate is reduced by 3.16% and the standard deviation is reduced by 1.841. In addition, the weight of each objective parameter was analyzed. The new predicted model has a better accuracy. It can evaluate and optimize the SQ exactly. The research methods and conclusions of this paper can be extended to the evaluation, prediction, and optimization of SQ of other motors.

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