Abstract
This chapter presents the recent development of neural network based output probability density function (PDF) shaping for stochastic distribution systems, where the purpose of controller design is to select proper feedback control laws so that the probability density function of the system output can be made to follow a target distribution shape. To start with, a survey on the stochastic distribution control (SDC) is given. This is then followed by the description of several neural networks approximations to the output PDFs. To illustrate the use of neural networks in the control design, an example of grinding process control using SDC theory is included here. © 2009 Springer London.
Original language | English |
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Publisher | Springer Nature |
Number of pages | 23 |
ISBN (Print) | 9781848825475 |
DOIs | |
Publication status | Published - 2009 |