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Company Approximation Using Incremental Constructive Feedforward Networks / SINGLE-HIDDEN-LAYER FEEDFORWARD NETWORKS / BP / NEURAL NETWORKS / UNIVERSAL APPROXIMATION USING INCREMENTAL CONSTRUCTIVE FEEDFORWARD NETWORKS / Section V. IEEE TRANSACTIONS ON NEURAL NETWORKS / / Country Singapore / / Currency USD / / / Facility Nanyang Technological University / / IndustryTerm threshold network / proposed incremental learning algorithms / ensemble computing / incremental algorithm / proposed incremental learning algorithm / The basic algorithm / inner product / function regression algorithms / incremental algorithms / large applications / sigmoid network / above constructed subset network / threshold networks / 1The network / incremental learning algorithms / feedforward network / feedforward networks / sequential learning algorithms / increment algorithms / approximated threshold network / mental network / learning algorithm / learning algorithms / specified computing environments / / Organization School of Electrical and Electronic Engineering / Nanyang Technological University / Singapore / / Person Lei Chen / Chee-Kheong Siew / Guang-Bin Huang / / Position pth learning SLFN Mp / / ProgrammingLanguage MATLAB / L / R / / Technology The basic algorithm / learning algorithm / SLFN Algorithm / neural network / sequential learning algorithms / incremental learning algorithms / increment algorithms / proposed incremental learning algorithm / previous incremental algorithms / SLFN algorithms / proposed SLFN algorithm / MRAN algorithms / function regression algorithms / simulation / proposed incremental learning algorithms / Digital Object Identifier / incremental algorithm / / URL http /
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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 17, NO. 4, JULY[removed]Universal Approximation Using Incremental Constructive Feedforward Networks