A general regression neural network

By Donald F. Specht. 1991

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Citations in the corpus

A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm (2013, methodology): “The generalized regression neural network (GRNN) which was developed by Specht [15] is a kind of probabilistic neural networks and also a powerful regression tool with a dynamic network structure.”
Applications of artificial neural networks for thermal analysis of heat exchangers – A review (2015, methodology): “GRNN configuration has radial basis layer and a linear layer as indicated by Specht [188].”
An approach to online identification of Takagi-Sugeno fuzzy models (2004, methodology): “Their use in online algorithms is only possible for the price of re-training the whole model structure and parameters with iterative and time-consuming procedures such as back-propagation [5], genetic algorithms [6]–[8], [10]–[12] or other nonlinear search techniques [1], [21], [29].”
Neural network models to predict cation exchange capacity in arid regions of Iran (2005, background): “E-mail: abbaspour@eawag.ch Received 25 March 2004; revised version accepted 8 November 2004 European Journal of Soil Science, August 2005, 56, 551–559 doi: 10.1111/j.1365-2389.2005.00698.x # 2005 British Society of Soil Science 551 Specht (1991).”
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2020, background): “Chaotic neural networks [8] and A general regression neural network [9].”
Toward Automatic Time-Series Forecasting Using Neural Networks (2012, background): “, it is a universal approximator) [39].”