eBook
Adaptive Filtering Under Minimum Mean P-Power Error Criterion
| Τίτλος: | Adaptive Filtering Under Minimum Mean P-Power Error Criterion |
|---|---|
| Περιγραφή: | Adaptive filtering still receives attention in engineering as the use of the adaptive filter provides improved performance over the use of a fixed filter under the time-varying and unknown statistics environments. This application evolved communications, signal processing, seismology, mechanical design, and control engineering. The most popular optimization criterion in adaptive filtering is the well-known minimum mean square error (MMSE) criterion, which is, however, only optimal when the signals involved are Gaussian-distributed. Therefore, many'optimal solutions'under MMSE are not optimal. As an extension of the traditional MMSE, the minimum mean p-power error (MMPE) criterion has shown superior performance in many applications of adaptive filtering. This book aims to provide a comprehensive introduction of the MMPE and related adaptive filtering algorithms, which will become an important reference for researchers and practitioners in this application area. The book is geared to senior undergraduates with a basic understanding of linear algebra and statistics, graduate students, or practitioners with experience in adaptive signal processing.Key Features: Provides a systematic description of the MMPE criterion. Many adaptive filtering algorithms under MMPE, including linear and nonlinear filters, will be introduced. Extensive illustrative examples are included to demonstrate the results. |
| Συγγραφείς: | Wentao Ma, Badong Chen |
| Resource Type: | eBook. |
| Θέματα: | Mathematical optimization--Data processing, Adaptive filters--Mathematical models, Mean field theory--Mathematical models |
| Categories: | COMPUTERS / Image Processing, MATHEMATICS / Probability & Statistics / General, TECHNOLOGY & ENGINEERING / Electrical |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 3892376 RelevancyScore: 975 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 974.776672363281 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive Filtering Under Minimum Mean P-Power Error Criterion – Name: Abstract Label: Description Group: Ab Data: Adaptive filtering still receives attention in engineering as the use of the adaptive filter provides improved performance over the use of a fixed filter under the time-varying and unknown statistics environments. This application evolved communications, signal processing, seismology, mechanical design, and control engineering. The most popular optimization criterion in adaptive filtering is the well-known minimum mean square error (MMSE) criterion, which is, however, only optimal when the signals involved are Gaussian-distributed. Therefore, many'optimal solutions'under MMSE are not optimal. As an extension of the traditional MMSE, the minimum mean p-power error (MMPE) criterion has shown superior performance in many applications of adaptive filtering. This book aims to provide a comprehensive introduction of the MMPE and related adaptive filtering algorithms, which will become an important reference for researchers and practitioners in this application area. The book is geared to senior undergraduates with a basic understanding of linear algebra and statistics, graduate students, or practitioners with experience in adaptive signal processing.Key Features: Provides a systematic description of the MMPE criterion. Many adaptive filtering algorithms under MMPE, including linear and nonlinear filters, will be introduced. Extensive illustrative examples are included to demonstrate the results. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wentao+Ma%22">Wentao Ma</searchLink><br /><searchLink fieldCode="AR" term="%22Badong+Chen%22">Badong Chen</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Mathematical+optimization--Data+processing%22">Mathematical optimization--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+filters--Mathematical+models%22">Adaptive filters--Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Mean+field+theory--Mathematical+models%22">Mean field theory--Mathematical models</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Image+Processing%22">COMPUTERS / Image Processing</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink><br /><searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Electrical%22">TECHNOLOGY & ENGINEERING / Electrical</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3892376 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 621.3815324 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Mathematical optimization--Data processing Type: general – SubjectFull: Adaptive filters--Mathematical models Type: general – SubjectFull: Mean field theory--Mathematical models Type: general Titles: – TitleFull: Adaptive Filtering Under Minimum Mean P-Power Error Criterion Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wentao Ma – PersonEntity: Name: NameFull: Badong Chen – PersonEntity: Name: NameFull: Wentao Ma – PersonEntity: Name: NameFull: Badong Chen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 – D: 16 M: 07 Type: profile Y: 2024 Identifiers: – Type: isbn-print Value: 9781032001654 – Type: isbn-electronic Value: 9781003176114 – Type: isbn-electronic Value: 9781040015926 – Type: isbn-electronic Value: 9781040015957 Titles: – TitleFull: Adaptive Filtering Under Minimum Mean P-Power Error Criterion Type: main |
| ResultId | 1 |