Abstract
A viable technique for sensitivity analysis in high-dimensional systems is described in the context of bio-inspired systems. The sensitivity analysis provides critical information about the system by indicating the dominant parameters that shape the output. This knowledge becomes particularly essential to have a better understanding of complex biological network of interactions. A notable feature of many high-dimensional systems is that a large portion of all parameters have little impact on the system outcome, thus yielding sparsity. The proposed algorithm leverages the sparse properties of systems analysed and is based on a heuristic two-stage elimination strategy. The implementation of the proposed algorithm yields substantial reduction of the total simulation cost by as much as 95% for a system composed of 562500 parameters over the conventional local sensitivity analysis while retaining its accuracy above 70%.
Original language | English |
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Title of host publication | 2016 IEEE International Conference on Electronics, Circuits and Systems, ICECS 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 181-184 |
Number of pages | 4 |
ISBN (Electronic) | 9781509061136 |
DOIs | |
Publication status | Published - 2016 |
Event | 23rd IEEE International Conference on Electronics, Circuits and Systems, ICECS 2016 - Monte Carlo, Monaco Duration: 11 Dec 2016 → 14 Dec 2016 |
Publication series
Name | 2016 IEEE International Conference on Electronics, Circuits and Systems, ICECS 2016 |
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Conference
Conference | 23rd IEEE International Conference on Electronics, Circuits and Systems, ICECS 2016 |
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Country/Territory | Monaco |
City | Monte Carlo |
Period | 11/12/16 → 14/12/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
Keywords
- biological systems
- high dimensional systems
- Sensitivity analysis
- sparse methods