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Growing evolutional deep echo state network
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DOI：10.1016/j.neucom.2024.128676.png)
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Abstract
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Deep echo state network (Deep ESN) is a powerful method for time-series prediction, but developing the optimal structure of Deep ESN remains challenging. A novel generative framework, growing evolutional Deep ESN (GE Deep ESN) is proposed, where networks are generated through alternating growth and evolution. A model compression method is implemented during the evolution phase to optimize the inner structure of the reservoirs, and to automatically determine reservoir size. A simple termination criterion of growth is introduced to determine network depth. The feasibility and superiority of GE Deep ESN were validated experimentally with two benchmark prediction tasks. For real-time trajectory prediction in photoelectric tracking systems, an iterative learning model based on GE Deep ESN is proposed, the real time prediction result showed that network evolution enhances the model's suitability for this task, and improves prediction precision.
Keywords:
Deep echo state network
Reservoir computing
Network sparsification
Time series forecast
Real-time trajectory prediction
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6.5
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2.5W
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6.5W
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