Layered feedforward neural network is relevant to empirical physical formula construction: A theoretical analysis and some simulation results

2005 ◽  
Vol 345 (1-3) ◽  
pp. 69-87 ◽  
Author(s):  
Nihat Yildiz
Keyword(s):  
Neural Network ◽  
Theoretical Analysis ◽  
Feedforward Neural Network ◽  
Simulation Results ◽  
Physical Formula

Force Ripple Suppression Research for Linear Motor Servo System Based on BP Neural Network

2014 ◽  
Vol 654 ◽  
pp. 191-195
Author(s):  
Rui Peng ◽  
Yue Nan Zeng ◽  
Jian Tang ◽  
Kang Ping Chen
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Theoretical Analysis ◽  
Permanent Magnet ◽  
Bp Neural Network ◽  
Detent Force ◽  
Simulation Results ◽  
Force Ripple ◽  
The Mathematical Model ◽  
Suppression Strategy

For the permanent magnet synchronous linear motor’s (PMSLM) force ripple, the mathematical model of detent force is established in this paper, and presents a suppression strategy based on neural network. By the designing of BP neural network force ripple observer, theoretical analysis shows can effectively restrain the force ripple. Simulation results show the correctness and validity of the suppression strategy.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Macro Modeling of V-Shaped Electro-Thermal MEMS Actuator with Human Error Factor

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 622
Author(s):  
Dongpeng Zhang ◽  
Anjiang Cai ◽  
Yulong Zhao ◽  
Tengjiang Hu
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Bp Neural Network ◽  
Human Error ◽  
Calculation Model ◽  
Fuzzy Mathematics ◽  
Ansys Simulation ◽  
Macro Modeling ◽  
Error Factor ◽  
Simulation Results

The V-shaped electro-thermal MEMS actuator model, with the human error factor taken into account, is presented in this paper through the cascading ANSYS simulation model and the Fuzzy mathematics calculation model. The Fuzzy mathematics calculation model introduces the human error factor into the MEMS actuator model by using the BP neural network, which effectively reduces the error between ANSYS simulation results and experimental results to less than 1%. Meanwhile, the V-shaped electro-thermal MEMS actuator model, with the human error factor included, will become more accurate as the database of the V-shaped electro-thermal actuator model grows.

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