Summary of Recurrent Stochastic Configuration Networks with Incremental Blocks, by Gang Dang and Dainhui Wang
Recurrent Stochastic Configuration Networks with Incremental Blocks
by Gang Dang, Dainhui Wang
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes block recurrent stochastic configuration networks (BRSCNs) to enhance the learning capacity and efficiency of recurrent stochastic configuration networks (RSCNs). RSCNs have shown promise in modeling nonlinear dynamic systems with order uncertainty. The authors introduce BRSCNs, which can add multiple reservoir nodes (subreservoirs) during construction. Each subreservoir has a unique structure, ensuring universal approximation property. The network’s echo state property is maintained by scaling the feedback matrix. Online updating of output weights and persistent excitation conditions facilitate parameter convergence. Numerical results demonstrate BRSCNs’ performance in time series prediction, nonlinear system identification, and industrial data predictive analysis, showcasing their potential for coping with complex dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recurrent stochastic configuration networks (RSCNs) are a type of machine learning model that’s good at handling complex systems. The new version called block RSCNs makes the network even better by adding more “reservoir nodes” during training. This helps the network learn and remember patterns in data. The authors also made sure the network works well with different types of data and scales its feedback to keep it stable. They tested this new model on some real-world problems and showed that it’s very good at making predictions. |
Keywords
» Artificial intelligence » Machine learning » Time series
