The study on self-adaptive predictive arithmetic based on RBF neural network applied in the proportion control of hydrogen and nitrogen in synthesis ammonia production

2011 ◽  
Author(s):  
Lijun Hao ◽  
Xiaolei Wei ◽  
Zhihong Wang ◽  
Shuai Zhou
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Ammonia Production ◽  
Self Adaptive

scholarly journals A Novel Positioning Algorithm Based on Self-adaptive Algorithm of RBF Network

2016 ◽  
Vol 10 (1) ◽  
pp. 141-148 ◽  
Author(s):  
Jin Ren ◽  
Jingxing Chen ◽  
Liang Feng
Keyword(s):  
Neural Network ◽  
Series Expansion ◽  
Taylor Series ◽  
Lower Cost ◽  
Rbf Neural Network ◽  
Taylor Series Expansion ◽  
Rbf Network ◽  
Location Algorithm ◽  
Positioning Algorithm ◽  
Self Adaptive

Much attention has been paid to Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. A Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed before to the performance of TSE highly depends on the initial estimation. In order to have more accurate and lower cost,a new Taylor-series expansion location algorithm based on Self-adaptive RBF neural network (SA-RBF-TSE) is proposed to estimate the initial value. The proposed algorithm is analysed and simulated with several other algorithms in this paper.

Self-Adaptive Fault Detection and Performance Assessment of a Rotary Actuator Based on Residual Analysis

2015 ◽  
Vol 764-765 ◽  
pp. 718-723
Author(s):  
Xin Li ◽  
Chen Lu ◽  
Zi Li Wang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Performance Assessment ◽  
Normal State ◽  
Rbf Neural Network ◽  
Residual Analysis ◽  
The Self ◽  
Rotary Actuator ◽  
And Performance ◽  
Self Adaptive

A rotary actuator that employs hydraulic oil as the power source has a direct rotary structure. It is an important structure and has been widely utilized in aircrafts and ships because of its advantages, including large torque/quality ratio, simple compact structure, and fast dynamic response. Huge damage may be caused when a rotary actuator breaks down during operation. However, only a few studies have focused on fault detection and performance assessment for rotary actuators. In this study, a method that detects the fault in and assesses the performance of the rotary actuator based on residual analysis is proposed. The data in normal state are utilized to build an observer with two radial basis function (RBF) neural networks. One RBF neural network is employed to estimate the expected output required to generate the residuals. The self-adaptive thresholds are obtained through the other RBF neural network. The residual is then inputted into the self-organizing mapping neural network trained by the residual values in normal state to normalize the performance of the rotary actuator into confidences values between 0 and 1. Finally, the detection and assessment of two typical faults of the rotary actuator are simulated. Results verify the efficiency of the proposed method.

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