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Suykens, J.A.K. and Vandewalle, J. (2000) Recurrent Least Squares Support Vector Machines. IEEE Transactions on Circuits and System—I, 47, 1109-1114.
http://dx.doi.org/10.1109/81.855471

has been cited by the following article:

• TITLE: Analysis of Ammonia Nitrogen Content in Water Based on Weighted Least Squares Support Vector Machine (WLSSVM) Algorithm

  AUTHORS: Jinwu Ju, Lanying Wang

  KEYWORDS: Support Vector Machine, Water Quality, Ammonia Nitrogen, Forecasting Model

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.9 No.2, February 18, 2016

  ABSTRACT: Determination of ammonia nitrogen content in water is the basic item of the environmental water pollution, and is the key index to evaluate the water quality. This article designs a water quality monitoring system based on the on-line automatic ammonia nitrogen monitoring system, and establishes a forecasting model based on the weighted least squares support vector machine algorithm. The weighted least squares support vector machine algorithm increases the weight parameter setting, improves the speed and accuracy of prediction learning, and improves the robustness. In this article, a comparison between neural network model and weighted least square support vector machine model is made, which shows that the weighted least squares support vector machine model has better prediction accuracy.