Adaptive Ensemble Kalman Estimation for Multi-Agent Model Parameters
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Solution Overview
Problem
Conventional methods for estimating parameters in multi-agent simulations are inefficient, leading to excessive calculation times due to the large number of parameters and their mutual influence, which can result in combinatorial explosion.
Innovation Solution
A parameter estimation device using an ensemble Kalman filter that adjusts the number of ensemble members and system noise across multiple iterations to improve estimation accuracy while minimizing calculation cost and avoiding local solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional brute-force parameter estimation is applied to multi-agent models, then all parameters can be estimated, but calculation time becomes enormous due to the large number of parameters and their mutual influence
Solution Approach 1:
The patent segments the parameter estimation problem into multiple iterations, where each iteration estimates a subset of parameters or refines estimates progressively. The ensemble Kalman filter processes parameters in stages rather than all at once, dividing the computational burden into manageable segments that reduce overall calculation time while maintaining estimation accuracy.
Solution Approach 2:
The patent applies preliminary action by using initial parameter estimates from previous iterations or preliminary runs to inform subsequent estimation processes. The ensemble Kalman filter uses prior knowledge and initial guesses to guide the estimation, avoiding exhaustive search from scratch and significantly reducing calculation time required to achieve accurate parameter estimates.
2Measurement precision
If the number of ensemble members in the ensemble Kalman filter is increased, then estimation accuracy is improved, but calculation cost increases
Solution Approach 1:
The patent applies dynamics by adaptively adjusting the number of ensemble members based on the iteration stage and estimation needs. In early iterations or when exploration is needed, more ensemble members are used. In later iterations or when convergence is approaching, the number of ensemble members is reduced. This dynamic adjustment optimizes the balance between estimation accuracy and calculation efficiency throughout the parameter estimation process.
Data Source
AI summary
A parameter estimation device repeatedly executes estimation process for estimating a parameter using an ensemble Kalman filter based on measured value data a plurality of times. Further, in the estimation process, the parameter estimation device performs at least one of increasing the number of ensemble members of the ensemble Kalman filter from the number of members in the estimation process related to the previous iteration and decreasing the magnitude of the system noise of the ensemble Kalman filter from the system noise in the estimation process related to the previous iteration, and sets an initial value of the parameter using the estimation result of the parameter by the estimation process related to the previous iteration.