scholarly journals PID Controller Design Based on Radial Basis Function Neural Networks for the Steam Generator Level Control

2016 ◽  
Vol 16 (5) ◽  
pp. 15-26 ◽  
Author(s):  
Kong Xiangsong ◽  
Chen Xurui ◽  
Guan Jiansheng
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Radial Basis Function ◽  
Steam Generator ◽  
Pid Control ◽  
Basis Function ◽  
Pid Controller ◽  
Rbf Neural Networks ◽  
Control Performance ◽  
Level Control

Abstract Steam generator level control system is a vital control system for the Pressurized Water Reactor (PWR). However, the steam generator level process is a highly nonlinear and non-minimum phase system, the conventional Proportional- Integral-Derivative (PID) control scheme with fixed parameters was difficult to obtain satisfactory control performance. The Radial Basis Function (RBF) Neural Networks based PID control strategy (RBFNN-PID) is proposed for the steam generator level control. This method can identify the mathematical model of the steam generator via the RBF neural networks, and then the PID parameters can be optimized automatically to accommodate the characteristic variation of the process. The optimal number of the hidden layer neurons is also discussed in this paper. The simulation results shows that the PID controller designed based on the RBF neural networks has good control performance on the steam generator level control.

Differential Evolution-Based Optimization of Kernel Parameters in Radial Basis Function Networks for Classification

2013 ◽  
Vol 4 (1) ◽  
pp. 56-80 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Neural Networks ◽  
Differential Evolution ◽  
Radial Basis Function ◽  
Basis Function ◽  
Learning Algorithm ◽  
Basis Functions ◽  
Second Phase ◽  
Rbf Neural Networks ◽  
Radial Basis ◽  
Two Phases

In this paper a two phases learning algorithm with a modified kernel for radial basis function neural networks is proposed for classification. In phase one a new meta-heuristic approach differential evolution is used to reveal the parameters of the modified kernel. The second phase focuses on optimization of weights for learning the networks. Further, a predefined set of basis functions is taken for empirical analysis of which basis function is better for which kind of domain. The simulation result shows that the proposed learning mechanism is evidently producing better classification accuracy vis-à-vis radial basis function neural networks (RBFNs) and genetic algorithm-radial basis function (GA-RBF) neural networks.

Descartes' Rule of Signs for Radial Basis Function Neural Networks

2002 ◽  
Vol 14 (12) ◽  
pp. 2997-3011 ◽  
Author(s):  
Michael Schmitt
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Lower Bounds ◽  
Basis Function ◽  
Rbf Neural Networks ◽  
Vc Dimension ◽  
Network Parameters ◽  
Number Of Zeros ◽  
Radial Basis

We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. The RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to infer that the Vapnik-Chervonenkis (VC) dimension and pseudodimension of these networks are no more than linear. This contrasts with previous work showing that RBF neural networks with two or more input nodes have superlinear VC dimension. The rules also give rise to lower bounds for network sizes, thus demonstrating the relevance of network parameters for the complexity of computing with RBF neural networks.

Product Identification Using Image Processing and Radial Basis Function Neural Networks

2015 ◽  
Vol 761 ◽  
pp. 120-124
Author(s):  
K.A.A. Aziz ◽  
Abdul Kadir ◽  
Rostam Affendi Hamzah ◽  
Amat Amir Basari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Architecture ◽  
Rbf Neural Networks ◽  
Specific Product ◽  
Product Identification ◽  
Radial Basis

This paper presents a product identification using image processing and radial basis function neural networks. The system identified a specific product based on the shape of the product. An image processing had been applied to the acquired image and the product was recognized using the Radial Basis Function Neural Network (RBFNN). The RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using a fast two-stage training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and the spread of RBF. In this paper, fixed spread value was used for every cluster. The system can detect all the four products with 100% successful rate using ±0.2 tolerance.

Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks

2016 ◽  
Vol 60 ◽  
pp. 151-164 ◽  
Author(s):  
Afshin Tatar ◽  
Saeid Naseri ◽  
Mohammad Bahadori ◽  
Ali Zeinolabedini Hezave ◽  
Tomoaki Kashiwao ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Ionic Liquids ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Networks ◽  
Radial Basis

scholarly journals Prediction of water formation temperature in natural gas dehydrators using radial basis function (RBF) neural networks

2016 ◽  
Vol 3 (2) ◽  
pp. 173-180 ◽  
Author(s):  
Tatar Afshin ◽  
Barati-Harooni Ali ◽  
Moslehi Hossein ◽  
Naseri Saeid ◽  
Bahadori Meysam ◽  
...  
Keyword(s):  
Neural Networks ◽  
Natural Gas ◽  
Radial Basis Function ◽  
Basis Function ◽  
Formation Temperature ◽  
Rbf Neural Networks ◽  
Water Formation ◽  
Radial Basis

Integral Separation PID Control in the Control of Lye Concentration

2012 ◽  
Vol 627 ◽  
pp. 489-493 ◽  
Author(s):  
Liang Huang ◽  
Shi Hai Zhao ◽  
Lin Cui
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
System Simulation ◽  
Dynamic Performance ◽  
Control Performance ◽  
Integral Separation ◽  
Simulation Results

For the requirements of the lye concentration in the mercerizing process, this article is designed for the control of the lye concentration in the integral separation PID controller, the controller is based on conventional PID control, Integral separation, and further improve the performance of the PID controller, improve the accuracy of the lye concentration control system. Simulation results show that: precise characteristics of the controller's existing conventional PID controller, and integral separation PID control system overshoot, improve the dynamic performance of the control system, enables the system to achieve satisfactory control performance.

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