Event-based intelligent control of saturated chemical plant using endomorphic neural network model

2002 ◽  
Author(s):  
Sung Hoon Jung ◽  
Tag Gon Kim ◽  
Kyu Ho Park
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Intelligent Control ◽  
Neural Network Model ◽  
Chemical Plant ◽  
Event Based

EVENT-BASED INTELLIGENT CONTROL USING ENDOMORPHIC NEURAL NETWORK MODEL

1995 ◽  
Vol 9 (5) ◽  
pp. 479-494
Author(s):  
SUNG HOON JUNG ◽  
TAG GON KIM ◽  
KYU HO PARK
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Intelligent Control ◽  
Neural Network Model ◽  
Event Based

Digital twin of the chip formation process

2020 ◽  
pp. 81-86
Author(s):  
Yu.G. Kabaldin ◽  
D.A. Shatagin ◽  
M.S. Anosov ◽  
A.M. Kuz'mishina
Keyword(s):  
Neural Network ◽  
Control System ◽  
Crystal Lattice ◽  
Network Model ◽  
Intelligent Control ◽  
Neural Network Model ◽  
Formation Process ◽  
Chip Formation ◽  
Intelligent Control System ◽  
The Relationship

The formation of chips during the processing of various materials was studied. The relationship between the type of chips, the type of crystal lattice of the material and the number of sliding systems is shown. A neural network model of chip formation is developed, which allows predicting the type of chips. An intelligent control system for the process of chip formation during cutting is proposed. Keywords: chip formation, crystal lattice, neural network model, type of chips. [email protected]

Test Model of Automobile Engine Magneto-Rheological Mount Based on RBF Neural Network

2013 ◽  
Vol 365-366 ◽  
pp. 450-453
Author(s):  
Jin Gang Ma ◽  
Peng Cheng Sheng ◽  
Hong Ying Song ◽  
Chun Lan Liang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Intelligent Control ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Test Model ◽  
Automobile Engine ◽  
New Ideas ◽  
Magneto Rheological ◽  
Hysteresis Characteristics

The nonlinear and hysteresis characteristics showed by magneto-rheological (MR) mount make it seem very difficult to establish a precise mathematical model. Based on the testing of MR mount dynamics, RBF neural network model can train and forecast the collected data. Analysis of comparing the predicting result of the RBF neural network model with the testing result shows that the trained RBF neural network model can exactly predict the dynamics of MR mount, and it provides some new ideas to implement the better intelligent control of the engine MR mount.

Sign in / Sign up

Export Citation Format

Share Document