NDLI: Direct adaptive control with multi-grid networks

Content Provider	IEEE Xplore Digital Library
Author	Pacut, A. Brudka, M.
Copyright Year	1996
Description	Author affiliation: Fac. of Electron. & Inf. Technol., Warsaw Univ. of Technol., Poland (Pacut, A.)
Abstract	Approximation of an unknown function can be effectively implemented with neural networks. A function represented by measurements obtained in real time is modeled by a Gaussian Radial Basis Function (RBF) network. A network proposed R. Sanner and J.J.B. Slottine (see IEEE Trans. on Neural Networks, vol.3, no.6, 1992) for this goal is modified to reduce the number of neurons necessary to guarantee the desired approximation accuracy. The proposed network consists of a series of grids defined as sets of neurons located, in the nodes of lattices of increasing density, with consecutive grids including all the previous ones. The grids of low density are responsible for approximation of global features of a function, and the grids of higher density are responsible for approximation of the local function properties. reduction of the network size is due to adaptive network architecture. At the beginning, the network consists of only the lowest density grid and all its neurons are idle, i.e., they just express a possibility of the neuron existence at a given position. During learning, the idle neurons of a grid can be transformed into the active ones to suit the local accuracy requirements, and if this is not sufficient, the next (higher density) lattice of idle neurons is included into the network, thus enabling for higher-frequency contents to be modeled. The proposed design was applied to a tracking control problem for two second order plants. Experiments showed good performance of control with considerably reduced number of neurons, as compared to a single-layer solution of of Sanner and Slottine.
Starting Page	386
Ending Page	391
File Size	732818
Page Count	6
File Format	PDF
ISBN	0780333349
DOI	10.1109/ISIE.1996.548452
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-06-17
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive control Neurons Lattices Radial basis function networks Information technology Electronic mail Neural networks Hardware Stability Error correction
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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