General regression neural network
Generalized regression neural network is a variation to radial basis neural networks. GRNN was suggested by D.F. Specht in 1991.
GRNN can be used for regression, prediction, and classification. GRNN can also be a good solution for online dynamical systems.
GRNN represents an improved technique in the neural networks based on the nonparametric regression. The idea is that every training sample will represent a mean to a radial basis neuron.Mathematical representation
where:
where is the squared euclidean distance between the training samples and the inputImplementation
GRNN has been implemented in many computer languages including MATLAB, R- programming language,Python and Node.js.
Neural networks can delineate non-linear patterns in data by combining with generalized linear models by considering distribution of outcomes. There have been several successful developments, including Poisson regression, ordinal logistic regression, quantile regression and multinomial logistic regression that described by Fallah in 2009.Advantages and disadvantages
Similar to RBFNN, GRNN has the following advantages:
- Single-pass learning so no backpropagation is required.
- High accuracy in the estimation since it uses Gaussian functions.
- It can handle noises in the inputs.
- It requires only less number of datasets.
The main disadvantages of GRNN are:
