Principal component analysis in application for filter tuning algorithm

Kacmajor, Tomasz; Michalski, Jerzy Julian

doi:10.1109/imws3.2011.6061853

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…The concept of filter characteristic transform for the purpose of filter tuning was introduced in [18,19]. In general, the discrete filter characteristic, symbolised by numbers, can be represented in different forms exposing different features, with the use of mathematical tools called transforms.…”

Section: Karhunen-loeve Transform Over Filter Characteristic -Principmentioning

confidence: 99%

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On Linear Mapping of Filter Characteristic to Position of Tuning Elements in Filter Tuning Algorithm

Michalski¹

2012

PIER

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Abstract-This work presents a novel approach in building a multidimensional approximator which is used as a linear operator for mapping the vector of detuned filter characteristic to the vector of deviations of tuning elements. This has been done for the purpose of using it in postproduction filter tuning algorithm. With the use of collected sets of deviations of tuning elements and filter characteristics corresponding to them, the least squares method (LSM) is applied to determine the matrix which realizes the linear mapping between these vectors. The matrix found in this method approximates the vectors of both spaces (filter characteristics and corresponding deviations of tuning elements). In tuning process this matrix is used to determine the vector of tuning element deviations for a given detuned filter characteristic read from Vector Network Analyzer. To increase the "quality" of linear operator filter characteristics are transformed with the use of Karhunen-Loeve transform (Principal Component Analysis). In contrast to non-linear artificial intelligence approximators used in filter tuning and published to-date, this method does not require a time-consuming training process. Filter tuning experiments have been performed and proved the correctness of the presented approach.

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Section: Karhunen-loeve Transform Over Filter Characteristic -Principmentioning

confidence: 99%

“…The numerical experiments demonstrating compression concept, which are described here, can be found in the work [18].…”

Section: Compression Mechanismmentioning

confidence: 99%