Self Organizing Neural Networks for the Identification Problem
Abstract
This work introduces a new method called Self Organizing Neural Network (SONN) algorithm and demonstrates its use in a system identification task. The algorithm constructs the network, chooses the neuron functions, and adjusts the weights. It is compared to the Back-Propagation algorithm in the identification of the chaotic time series. The results shows that SONN constructs a simpler, more accurate model. requiring less training data and epochs. The algorithm can be applied and generalized to appilications as a classifier.
Cite
Text
Tenorio and Lee. "Self Organizing Neural Networks for the Identification Problem." Neural Information Processing Systems, 1988.Markdown
[Tenorio and Lee. "Self Organizing Neural Networks for the Identification Problem." Neural Information Processing Systems, 1988.](https://mlanthology.org/neurips/1988/tenorio1988neurips-self/)BibTeX
@inproceedings{tenorio1988neurips-self,
title = {{Self Organizing Neural Networks for the Identification Problem}},
author = {Tenorio, Manoel Fernando and Lee, Wei-Tsih},
booktitle = {Neural Information Processing Systems},
year = {1988},
pages = {57-64},
url = {https://mlanthology.org/neurips/1988/tenorio1988neurips-self/}
}