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Research on Application of Bounded Derivative Neural Network for Nonlinear Predictive Control

Author: ShiYiPing
Tutor: ZhaoJun
School: Zhejiang University
Course: Systems Engineering
Keywords: Bounded derivative neural network Predictive Control Gain scheduling
CLC: TP183
Type: Master's thesis
Year: 2008
Downloads: 55
Quote: 2
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Abstract


The learning process of the neural network is an optimization process , depending on the error information to reasonably select the network weights . In the input and output of the network model of the relationship established , the most important guiding information is a derivative of the relationship between the input and output , this derivative relations can only properly establish the corresponding relationship between input and output values ??. Currently, the optimization of the neural network learning algorithm , using only the network output data error as guidance information for network training , and no effective way of bringing a derivative of the model , lead to poor network generalization ability , not practical . Simple minimum output data error as standard to guide network is trained , its essence is only accurate interpolation of the sample data , and the non - smooth interpolation to generate a large error , thus the non - sample data . Therefore , the network training process , in addition to considering the information provided by the sample data , should also take into account the derivative of the model , and turn it into a form of constraint is introduced into the the network learning optimization process which . This paper studies the neural network training algorithms with constraint , and neural network applications to nonlinear model predictive control . The main content of this paper and innovations include : (1) using two different methods with constrained training of the neural network , through penalty function neural network and bounded derivative of the neural network training results , the analysis of these two methods exist advantages and disadvantages. (2 ) using the bounded derivative neural network \\ modeling of the steady-state model , combined with the DMC algorithm , real-time forecast model gain scheduling , to some extent, improve the quality of control . (3) by changing the bounded derivative of neural networks of the network structure , the network from the static network changes for dynamic network , while still making the network maintained its static has the derivative constraints capacity , so that the bounded derivative neural network has a the modeling capability can be the object of the static and dynamic two-part .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing
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