Neural network approaches to some model flow control problems

1997 ◽  
Author(s):  
Yongseung Cho ◽  
Ramesh Agarwal ◽  
Kyungmoon Nho ◽  
Yongseung Cho ◽  
Ramesh Agarwal ◽  
...  
Keyword(s):  
Neural Network ◽  
Flow Control ◽  
Control Problems ◽  
Model Flow ◽  
Network Approaches

scholarly journals Outlier detection in scatterometer data: neural network approaches

2003 ◽  
Vol 16 (3-4) ◽  
pp. 419-426 ◽  
Author(s):  
Robert J. Bullen ◽  
Dan Cornford ◽  
Ian T. Nabney
Keyword(s):  
Neural Network ◽  
Outlier Detection ◽  
Network Approaches

Feasibility Study on the Use of a Voice Coil Motor Direct Drive Flow Rate Control Valve

2009 ◽  
Author(s):  
Shuai Wu ◽  
Richard Burton ◽  
Zongxia Jiao ◽  
Juntao Yu ◽  
Rongjie Kang
Keyword(s):  
Neural Network ◽  
Flow Control ◽  
Flow Rate ◽  
Rate Control ◽  
Network Flow ◽  
Voice Coil Motor ◽  
Direct Drive ◽  
Hydraulic Test ◽  
Servo Valve ◽  
Flow Rate Control

This paper considers the feasibility of a new type of voice coil motor direct drive flow control servo valve. The proposed servo valve controls the flow rate using only a direct measurement of the spool position. A neural network is used to estimate the flow rate based on the spool position, velocity and coil current. The estimated flow rate is fed back to a closed loop controller. The feasibility of the concept is established using simulation techniques only at this point. All results are validated by computer co-simulation using AMESim and Simulink. A simulated model of a VCM-DDV (Voice Coil Motor-Direct Drive Valve) and hydraulic test circuit are built in an AMESim environment. A virtual digital controller is developed in a Simulink environment in which the feedback signals are received from the AMESim model; the controller outputs are sent to the VCM-DDV model in AMESim (by interfacing between these two simulation packages). A LQR (Linear Quadratic Regulator) state feedback and nonlinear compensator controller for spool position tracking is considered as this is the first step for flow control. A flow rate control loop is subsequently included via a neural network flow rate estimator. Simulation results show that this method could control the flow rate to an acceptable degree of precision, but only at low frequencies. This kind of valve can find usage in open loop hydraulic velocity control in many industrial applications.

scholarly journals International agricultural trade forecasting using machine learning

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Munisamy Gopinath ◽  
Feras A. Batarseh ◽  
Jayson Beckman ◽  
Ajay Kulkarni ◽  
Sei Jeong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Agricultural Trade ◽  
Supervised Machine Learning ◽  
Trade Patterns ◽  
Subjective Assessments ◽  
History Of ◽  
Network Approaches ◽  
Traditional Approaches

Abstract Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

Neural Network Approaches to Data Fusion

1995 ◽  
pp. 819-826 ◽  
Author(s):  
J. Yim ◽  
S. S. Udpa ◽  
L. Udpa ◽  
M. Mina ◽  
W. Lord
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Network Approaches
Sign in / Sign up

Export Citation Format

Share Document