Blind separation of signals with mixed kurtosis signs using threshold activation functions
A parameterized activation function in the form of an adaptive threshold for a single-layer neural network, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This activation function is particularly simple to implement, since it neither uses hyperbolic...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2001-05, Vol.12 (3), p.618-624 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Mathis, H. von Hoff, T.P. Joho, M. |
| description | A parameterized activation function in the form of an adaptive threshold for a single-layer neural network, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This activation function is particularly simple to implement, since it neither uses hyperbolic nor polynomial functions, unlike most other nonlinear functions used for blind separation. For some specific distributions, the stable region of the threshold parameter is derived, and optimal values for best separation performance are given. If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods. |
| doi_str_mv | 10.1109/72.925565 |
| format | Article |
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If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods.</description><identifier>ISSN: 1045-9227</identifier><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 1941-0093</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/72.925565</identifier><identifier>PMID: 18249895</identifier><identifier>CODEN: ITNNEP</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Activation ; Adaptive algorithm ; Blinds ; Equations ; Gaussian ; Higher order statistics ; Information processing ; Kurtosis ; Maximum likelihood estimation ; Neural networks ; Optimization ; Polynomials ; Separation ; Separation processes ; Signal processing ; Source separation ; Thresholds</subject><ispartof>IEEE transaction on neural networks and learning systems, 2001-05, Vol.12 (3), p.618-624</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods.</description><subject>Activation</subject><subject>Adaptive algorithm</subject><subject>Blinds</subject><subject>Equations</subject><subject>Gaussian</subject><subject>Higher order statistics</subject><subject>Information processing</subject><subject>Kurtosis</subject><subject>Maximum likelihood estimation</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Polynomials</subject><subject>Separation</subject><subject>Separation processes</subject><subject>Signal processing</subject><subject>Source separation</subject><subject>Thresholds</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0UtLxDAQAOAgio_Vg1cPEjwoHqrJ5NUcdfEFghe9eCltk-5Gu-2atD7-vVm7KHhQCCTMfDMMGYR2KTmhlOhTBScahJBiBW1SzWlCiGar8U24SDSA2kBbITwRQrkgch1t0BS4TrXYRI_ntWsMDnae-7xzbYPbCgc3afI64DfXTfHMvVuDn3vftcGFr1zAfXDNBHdTb8O0rQ3Oy869DvVV35SLR9hGa1XsYneW9wg9XF7cj6-T27urm_HZbVJygC6B1MapuNS0MkWc1RQFq4ATVUFJWQGcGQIgY4AwbUSZFwwKKuMBbmKIjdDR0Hfu25fehi6buVDaus4b2_Yh05RLLkHBv1IxxhVQmkZ5-KeElCpJBfsfSsmIFiTCg1_wqe394pszDSSVinMe0fGASt-G4G2Vzb2b5f4joyRbrDpTkA2rjnZ_2bAvZtb8yOVuI9gbgLPWfqeX1Z-Tg6oD</recordid><startdate>20010501</startdate><enddate>20010501</enddate><creator>Mathis, H.</creator><creator>von Hoff, T.P.</creator><creator>Joho, M.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1045-9227 |
| ispartof | IEEE transaction on neural networks and learning systems, 2001-05, Vol.12 (3), p.618-624 |
| issn | 1045-9227 2162-237X 1941-0093 2162-2388 |
| language | eng |
| recordid | cdi_pubmed_primary_18249895 |
| source | IEEE Electronic Library Online |
| subjects | Activation Adaptive algorithm Blinds Equations Gaussian Higher order statistics Information processing Kurtosis Maximum likelihood estimation Neural networks Optimization Polynomials Separation Separation processes Signal processing Source separation Thresholds |
| title | Blind separation of signals with mixed kurtosis signs using threshold activation functions |
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