Abstract
This paper presents a new model for the control of multivariable output probability density function (PDF). Firstly, a Multi-Layer Perceptron (MLP) neural network is adopted to approximate the static output PDF of the MIMO systems. Nonlinear principal component analysis (NLPCA) is then used to reduce the order of the obtained static neural network model and the dynamics of the system is considered based on the lower-order model. After this, an integrated solution is provided to set up the system with lower-order dynamics for the purpose of stochastic distribution control. The controller design is then presented in detail. Finally, a simulation example is given to demonstrate the effectiveness of the method and encouraging results have been obtained.
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© 2008 Springer-Verlag Berlin Heidelberg
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Wang, W., Xiong, Y.L., Wang, H., Yue, H. (2008). MIMO Probability Density Function Control Using Simple LOG-MLP Neural Networks. In: Xiong, C., Liu, H., Huang, Y., Xiong, Y. (eds) Intelligent Robotics and Applications. ICIRA 2008. Lecture Notes in Computer Science(), vol 5315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88518-4_88
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DOI: https://doi.org/10.1007/978-3-540-88518-4_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88516-0
Online ISBN: 978-3-540-88518-4
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