An Adaptive Approximate Model Neural Network Controller for EAF Electrode Regulator System

2013 ◽  
Vol 373-375 ◽  
pp. 1432-1436 ◽  
Author(s):  
Hong Ge Zhao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Approximate Model ◽  
Equivalent Model ◽  
Nonlinear Controller ◽  
Output Measurement ◽  
Neural Network Controller ◽  
Network Controller ◽  
The Neural Networks ◽  
Robust Adaptive

This paper proposes a robust adaptive neural network controller (RANNC) for electrode regulator system. An equivalent model in affine-like is derived for electrode regulator system. Then, the nonlinear control law is derived directly based on the affine-like equivalent model identified with neural networks, which avoids complex control development and intensive computation. Pretraining is not required and the weights of the neural networks used in adaptive control are directly updated online based on the input-output measurement. The proposed nonlinear controller is verified by computer simulations.

scholarly journals NN-Based Approximate Model Control for the EAF Electrode Regulator System

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Hongge Zhao
Keyword(s):  
Approximate Model ◽  
Equivalent Model ◽  
Neural Network Controller ◽  
Network Controller ◽  
Control Scheme ◽  
Model Control ◽  
System Requirements ◽  
Robust Adaptive ◽  
The Stability ◽  
Nonlinear Control Law

This paper proposes a robust adaptive neural network controller (RANNC) for electrode regulator system. According to the characteristics of electrode regulator system, an affine-like equivalent model is first derived. Then, the nonlinear control law is derived directly based on the affine-like equivalent model identified with neural networks, which avoids complex control development and intensive computation. The control scheme is simple enough that it can be implemented on an automotive microcontroller system, and the performance meets the system requirements. The stability of the system is established by the Lyapunov method. Several simulations illustrate the effectiveness of the controller.

scholarly journals Neural Network Controller for Power Electronics Circuits

2017 ◽  
Vol 6 (2) ◽  
pp. 49
Author(s):  
K.J. Rathi ◽  
M. S. Ali
Keyword(s):  
Neural Network ◽  
Power Electronics ◽  
Intelligent Control ◽  
Output Voltage ◽  
Network Applications ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
The Neural Networks ◽  
Neural Network Applications

Artificial Intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics. This paper explores the perspective of neural network applications in the intelligent control for power electronics circuits. The Neural Network Controller (NNC) is designed to track the output voltage and to improve the performance of power electronics circuits. The controller is designed and simulated using MATLAB-SIMULINK

On-line control for optimal ignition timing using the pseudolinear radial basis function and the local linear model tree

2005 ◽  
Vol 219 (2) ◽  
pp. 227-240 ◽  
Author(s):  
M Beham ◽  
D L Yu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Linear Model ◽  
Basis Function ◽  
Ignition Timing ◽  
Neural Network Controller ◽  
Look Up Table ◽  
Local Linear ◽  
Network Controller

A new generation of engines demands new control strategies. The increased number of control variables of variable valve timing engines results in complexity of conventional control structures. This necessitates the integration of new technologies for optimal control of the ignition timing. This paper presents a neural network controller for ignition timing that uses two recently proposed new neural network structures—a pseudolinear radial basis function (PLRBF) network and a local linear model tree (LOLIMOT) network. Tests showed that the relative load signal is not necessary to evaluate the ignition angle, and therefore no air mass meter is necessary. The two neural networks are compared with a conventional look-up table control structure. The network controller improves the conventional look-up table method for calibration by comparison with bilateral look-up tables. The neural controller is implemented and tested in a research car. Experimental results show that the neural networks are very effective in mapping non-linearity. The design of the neural network controller simplifies the structure drastically.

Multiple Microprocessor Neural Network Master-Slave Control System in the Application of the Coaxial Control System

2014 ◽  
Vol 568-570 ◽  
pp. 1045-1048
Author(s):  
Gang Yuan Mao ◽  
Hui Kang Liu ◽  
Yin Xian Yang ◽  
Fei Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motor Control ◽  
Control System ◽  
Design Parameters ◽  
Neural Network Controller ◽  
Network Controller ◽  
Motor Control System

This article is mainly based on the host parameters design a neural network controller, using neural networks enable from the machine's speed and torque have followed the host, so as to achieve a master-slave follow mode. And then, depending on the design parameters for simulation and operation, and puts forward the two coaxial motor control system speed concept ring adopts a master-slave control to achieve the same speed.

scholarly journals Tax Fraud Detection through Neural Networks: An Application Using a Sample of Personal Income Taxpayers

2019 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
César Pérez López ◽  
María Delgado Rodríguez ◽  
Sonia de Lucas Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Income Tax ◽  
Fraud Detection ◽  
Personal Income ◽  
Personal Income Tax ◽  
Tax Returns ◽  
The Neural Networks ◽  
Tax Fraud ◽  
Efficiency Rate

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

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