### Abstract

A theory is introduced for a multi-layered Local Output Gamma Feedback Neural Network (LOGF-NN) within the Locally Recurrent Globally Feedforward neural networks paradigm. It is developed for the classification and prediction tasks for spatio-temporal systems, and allows the representation of different time scales through the incorporation of a gamma memory. The update equations for the feedforward and temporal weights and parameters are derived through the Back-propagation Through Time (BTT) learning algorithm. As a demonstration, it is applied to the benchmark problem of single-step sunspot series prediction, and is compared to other neural network (Weight Elimination Neural Network: WNET) and statistical (Linear and Threshold AutoRegressive: TAR) methods. As a measure of prediction accuracy, the Average Relative Variance (ARV) is used. The proposed LOGF-NN approach's performance is comparable to the TAR method and outperforms the Linear AR and the WNET approaches.

Original language | English (US) |
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Pages | 337-342 |

Number of pages | 6 |

State | Published - Jan 1 1998 |

Event | Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA Duration: May 4 1998 → May 9 1998 |

### Other

Other | Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) |
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City | Anchorage, AK, USA |

Period | 5/4/98 → 5/9/98 |

### ASJC Scopus subject areas

- Software

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## Cite this

*Local output gamma feedback neural network*. 337-342. Paper presented at Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3), Anchorage, AK, USA, .