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However, IIR filters might have a multi-modal error surface.
Shahaf, N. et al. Constructing a mass measurement error surface to improve automatic annotations in liquid chromatography/mass spectrometry based metabolomics.
The form error, surface topography, material phase transformation, and cutting force characteristics were investigated experimentally.
The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling.
These models can interpolate among the different experimental conditions and can also describe the error surface when fitting life prediction models to the data.
Since the problem of the error surface of designing digital IIR filters is generally nonlinear and multimodal, global optimization techniques are required in order to avoid local minima.
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When a much larger sample was reviewed, a programming error surfaced.
A heuristic "annealing schedule" is presented that is effective in finding global minima of error surfaces.
The predefined performance bounds, which characterize the convergence rate, maximum overshoot, and steady-state response of control errors, are integrated with error surfaces in consideration of underactuated constraints.
In the design of adaptive IIR filters, the multi-modal nature of the error surfaces can limit the use of gradient-based and other iterative search methods.
Furthermore, a delay-independent tracking scheme using nonlinearly transformed error surfaces is simply designed without the knowledge of nonlinear bounding functions of model nonlinearities, the adaptive technique, and the calculation of repeated time derivatives of certain signals.
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