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Signal analysis techniques (supervised-type learning filter in combination with a Chebyshev filter) constrained and tested by independent accelerometer data were used to process noisy GPS measurements of oscillations of 40 m long steel footbridge excited by coordinated jumps of a group of people.
The progression of the ILC learning filter brings an additional degree of freedom for the learning filter design with proven stability properties.
Zero-phase or time-reversal filtering ILC is applied to track smoothened impulse, where the learning filter is progressively updated while trajectory learning proceeds.
Based on the modeling result of control system, gain scheduling technique is further incorporated in the learning filter design of ILC loop to speed up the learning convergence.
Since the error convergence rate depends on the learning filter, which ideally should invert the plant dynamics, the challenge lies in creating the ILC learning filters that approximate the plant inverse without having the plant model.
A non-causal transversal finite impulse response (FIR) filter is used as the ILC learning filter, and the impulse response coefficients of the FIR filter are designed according to the asymptotically stable and monotonically convergent criterion in time domain.
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The algorithm is evaluated by learning filters for locating specific areas of plant leaves.
To facilitate this procedure, this paper proposes a systematics approach to design learning filters for arbitrary-order ILC with guaranteed convergence, robustness and ease of tuning.
The key in ILC is to design learning filters with guaranteed convergence and robustness, which usually involves lots of tuning effort especially in high-order ILC.
Subsequently, we performed a fine-tuning of the earlier learned filters by further training the network with labelled thermal images.
These learned filters capture image information at a different levels in the form of low-level edges, mid-level edge junctions, and high-level object parts.
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