On-line identification method and device for parameters of DOC temperature rise model based on piecewise adaptive variable forgetting factor recursive least square
By using a piecewise adaptive variable forgetting factor recursive least squares algorithm, combined with particle swarm optimization and Nelder-Mead algorithm to optimize the DOC temperature rise model parameters, the problem of performance imbalance of traditional methods under different working conditions is solved, and higher recognition accuracy and real-time tracking capability are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU UNIV
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-17
AI Technical Summary
Traditional recursive least squares algorithms with fixed forgetting factors exhibit performance imbalances under different operating conditions, making it difficult to balance tracking speed and steady-state accuracy.
A piecewise adaptive variable forgetting factor recursive least squares algorithm is adopted, which uses particle swarm optimization to find the optimal parameter vector, combines Nelder-Mead simplex algorithm for local refinement, and performs RLS recursion based on the forgetting factor matched to the operating conditions to identify the parameters of the DOC temperature rise model.
It improves the overall accuracy of parameter identification in the DOC temperature rise model, solves the performance imbalance problem of traditional methods under different working conditions, and enhances identification accuracy and real-time tracking capability.
Smart Images
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