General regression neural network
Intoduction
Generalized regression neural network (GRNN) is a variation to radial basis neural networks (RBFNN). GRNN was suggested by D.F. Specht in 1991.[1].
GRNN can be used for regression, prediction, and classification. GRNN can also be a good solution for online dynamic systems.
GRNN represents an improved technique in the neural networks based on the non-paramertic regression. The basic idea is that every training sample will represents a mean to a radial basis neuron[2].
Mathematical represntion of the GRNN[3]:
where is the prediction value of input .
where is the distance between the training samples and the input .
GRNN has been implemented in many software including Matlab[4] and R- programming language.
Advantages and Disadvatnages of GRNN:
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.
The main disadvantages of GRNN are:
- Its size can grow to huge size which computationally expensive.
References
- ^ "A general regression neural network - IEEE Xplore Document". Ieeexplore.ieee.org. 2002-08-06. Retrieved 2017-03-13.
- ^ https://minds.wisconsin.edu/bitstream/handle/1793/7779/ch2.pdf?sequence=14
- ^ https://minds.wisconsin.edu/bitstream/handle/1793/7779/ch2.pdf?sequence=14
- ^ https://au.mathworks.com/help/nnet/ug/generalized-regression-neural-networks.html