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We present an optimized method for sensor lag correction using a transfer function, based on the optimization of three correction parameters: moving average window size, transfer function setup, and linear time shift.
We applied the algorithm to the sensory spaces of a number of simple, simulated agents with different motor parameters, moving in two-dimensional mazes.
It uses autoregressive parameters, moving average parameters and the number of differencing passes to describe a series in which a pattern is repeated over time.
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Both the frequency and amplitude of the stable oscillations in region C vary as the parameters move around within it.
The model is specially designed to let all oscillator parameters move freely and find their most probable position by using a least square fitting analysis procedure.
As the parameters move from the top to the bottom of region D, the stable limit cycle continues to shrink while the unstable limit cycle grows.
The frequency of oscillations decreases to zero as the parameters move toward the SNIC bifurcation curve (the boundary between regions B and C) and increases as the parameters are decreased away from this curve.
de Pedraza et al. (2010) Methodological WageIndicator data and Labour Force Survey in Spain Statistical analysis of labour market issues Some parameters moved in the right direction after weighting, but other methods need to be considered, including propensity score adjustment or a priori measures, such as marketing and targeting.
CRs decline as CS parameters move away from the trained values.
As the parameters move from the location of the maximal limiting variance to the minimum, the variance monotonically decreases.
To represent this, we may either introduce absolute criteria that prevent the optimisation algorithm from assigning out-of-bounds values, or we can produce a penalty function, which increases as the parameters move further into areas of unacceptable solutions.
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CEO of Professional Science Editing for Scientists @ prosciediting.com