Neural networks based probability density function control for stochastic systems: Recent Advances in Intelligent Control Systems

Xubin Sun, Jinliang Ding, Tianyou Chai, Hong Wang

    Research output: Book/ReportAnthology

    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 languageEnglish
    PublisherSpringer Nature
    Number of pages23
    ISBN (Print)9781848825475
    DOIs
    Publication statusPublished - 2009

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