Details

Autor(en) / Beteiligte

• Burg, Thomas
• Tschichold-Gürman, Nadine

Titel

An extended neuron model for efficient timeseries generation and prediction

Ist Teil von

• Artificial Neural Networks — ICANN'97, 2005, p.1005-1010

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2005

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Modelling of spatio-temporal patterns with neural networks is an important task for a large number of applications which require adaptive control. In this paper, the use of an extended neuron model in neural networks is proposed to achieve a given dynamic network behaviour. The new neuron model is based on the implementation of exponential excitation decay and the introduction of temporal refractoriness of the neuron output as observed in biological nerve cells. A learning algorithm based on error-backpropagation for the resulting network is derived. A benchmark test on prediction of the chaotic Macky-Glass differential equation and real-live experiments with controlling the movement of a walking machine leg are performed. The results suggest superior time-series modelling ability of the presented approach in terms of network trainability and computation complexity.