scholarly journals Model Bridging: Connection Between Simulation Model and Neural Network

2021 ◽  
pp. 389-405
Author(s):  
Keiichi Kisamori ◽  
Keisuke Yamazaki ◽  
Yuto Komori ◽  
Hiroshi Tokieda
Keyword(s):  
Neural Network ◽  
Simulation Model

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.

Fault Detection for an Aileron Actuator under Variable Conditions Based on Bi-Step Neural Network

2015 ◽  
Vol 764-765 ◽  
pp. 740-746
Author(s):  
Hang Yuan ◽  
Chen Lu ◽  
Ze Tao Xiong ◽  
Hong Mei Liu
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Simulation Model ◽  
Working Conditions ◽  
Detection Method ◽  
Fault Tolerant ◽  
Adaptive Threshold ◽  
Residual Error ◽  
The Other ◽  
Aerodynamic Loads

Fault detection for aileron actuators mainly involves the enhancement of reliability and fault tolerant capability. Considering the complexity of the working conditions of aileron actuators, a fault detection method for an aileron actuator under variable conditions is proposed in this study. A bi-step neural network is utilized for fault detection. The first neural network, which is employed as the observer, is established to monitor the aileron actuator and generate the residual error. The other neural network generates the corresponding adaptive threshold synchronously. Faults are detected by comparing the residual error and the threshold. In considering of the variable conditions, aerodynamic loads are introduced to the bi-step neural network. The training order spectrums are designed. Finally, the effectiveness of the proposed scheme is demonstrated by a simulation model with different faults.

Operational Parameters and Back Propagation Neural Network (BPNN) Simulation Model of Integration Membrane Bioreactor (IMBR) Treating Sewage from Ship

2011 ◽  
Vol 356-360 ◽  
pp. 1042-1045
Author(s):  
Yue Shi ◽  
Liang Guo ◽  
Jun Zhou ◽  
Run Bai ◽  
Xin Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Membrane Bioreactor ◽  
Ph Value ◽  
Back Propagation ◽  
Influence Factors ◽  
Back Propagation Neural Network ◽  
Living Environment ◽  
Operational Parameters ◽  
Scale Experiment

Nowadays, in order to meet the new standard of IMO for sewage discharged from ship treatment, membrane bioreactor (MBR) was widely used in this field. In this study, a novel bioreactor named integration membrane bioreactor (IMBR) was used to treat sewage from ship. A lab scale experiment was conducted to find the best controlling strategy of operation. The results were as follows: The IMBR had strong adaptability and effluent stability under wide change in VLR which was from 1.2kg/m3.d to 4.3kg/m3.d; The HRT of the IMBR was suggested to be controlled around 6h; The IMBR operator was better in alkali-resistant and weaker in acid-proof, which implied the pH of suitable living environment for aerobic microbe should be higher than 6.5. At the same time, a simulation model of operational parameters was established based on theory of back propagation neural network (BPNN). The simulation model realizes prediction of which were the key impact factor and optimum operational parameters of the IMBR system. Each parameter influencing the performance of the reactor was compared using the method of partitioning connection weights (PCW). The weight of the influence factors was pH value> DO>influent COD in the experimental range.

RECURSIVE NEURAL NETWORK MODEL OF CATAMARAN MANOEUVRING

2021 ◽  
Vol 154 (A3) ◽  
Author(s):  
L Moreira ◽  
C Guedes Soares
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Simulation Model ◽  
Network Model ◽  
Neural Network Model ◽  
Full Scale ◽  
Passenger Transport ◽  
Recursive Neural Network ◽  
Rudder Angle ◽  
Scale Data

A neural network model to simulate catamaran manoeuvres is proposed as an alternative to the traditional methodology of developing manoeuvring mathematical models. Data obtained in full-scale trials with a real ship are used to train the model. By recording full-scale trials of catamaran manoeuvres it is possible to generate a neural network model which will allow the prediction of the catamaran manoeuvring performance under different conditions. A Recursive Neural Network (RNN) manoeuvring simulation model is proposed and applied to a catamaran in this specific case. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. Two types of manoeuvres are simulated: tactical circles and zigzags. The results between the full-scale data and the simulations are compared in order to analyze and determine the accuracy of the RNN. The study is performed for a catamaran operating in the Tagus estuary for passenger transport to and from Lisbon.

Recursive Neural Network Model Of Catamaran Manoeuvring

2012 ◽  
Vol 154 (A3) ◽  
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Simulation Model ◽  
Network Model ◽  
Neural Network Model ◽  
Full Scale ◽  
Passenger Transport ◽  
Recursive Neural Network ◽  
Rudder Angle ◽  
Scale Data

A neural network model to simulate catamaran manoeuvres is proposed as an alternative to the traditional methodology of developing manoeuvring mathematical models. Data obtained in full-scale trials with a real ship are used to train the model. By recording full-scale trials of catamaran manoeuvres it is possible to generate a neural network model which will allow the prediction of the catamaran manoeuvring performance under different conditions. A Recursive Neural Network (RNN) manoeuvring simulation model is proposed and applied to a catamaran in this specific case. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. Two types of manoeuvres are simulated: tactical circles and zigzags. The results between the full-scale data and the simulations are compared in order to analyze and determine the accuracy of the RNN. The study is performed for a catamaran operating in the Tagus estuary for passenger transport to and from Lisbon.

Research on Electric Vehicle Road Identification Method Based on RBF Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 1413-1416
Author(s):  
Zhi Yu Huang ◽  
Jia Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Simulation Model ◽  
Rbf Neural Network ◽  
Relevant Information ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
The Road ◽  
Road Condition ◽  
Motor Control System

Accurately identifying road condition can send relevant information to the motor control system, so that control system of the motor can adjust the control strategy timely, eventually, the intelligent and optimal control of electric vehicles is realized. In this paper, according to these mathematical model, the permanent magnet synchronous motors simulation model and vehicles simulation model are proposed. Then, output torque of motor and speed of motor are served as the input of RBF neural network, which helps road condition to be identified. The simulation result shows that the road condition is well identified by proposed method based on RBF neural network.

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