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Nonlinear FIR adaptive filters with a gradient adaptive amplitude in the nonlinearity
A nonlinear gradient descent (NGD) learning algorithm with an adaptive amplitude of the nonlinearity is derived for the class of nonlinear finite impulse response (FIR) adaptive filters (dynamical perceptron). This is based on the adaptive amplitude backpropagation (AABP) algorithm for large-scale n...
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| Published in: | IEEE signal processing letters 2002-08, Vol.9 (8), p.253-255 |
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| Main Authors: | , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c381t-eca8e312f9a03e9199e1aa36cf08c424b5be9ea5d3f8b3144238aa4c036c0e7c3 |
| container_end_page | 255 |
| container_issue | 8 |
| container_start_page | 253 |
| container_title | IEEE signal processing letters |
| container_volume | 9 |
| creator | Hanna, A.I. Mandic, D.P. |
| description | A nonlinear gradient descent (NGD) learning algorithm with an adaptive amplitude of the nonlinearity is derived for the class of nonlinear finite impulse response (FIR) adaptive filters (dynamical perceptron). This is based on the adaptive amplitude backpropagation (AABP) algorithm for large-scale neural networks. The amplitude of the nonlinear activation function is made gradient adaptive to give the adaptive amplitude nonlinear gradient descent (AANGD) algorithm, making the AANGD suitable for processing nonlinear and nonstationary input signals with a large dynamical range. Experimental results show the AANGD algorithm outperforming the standard NGD algorithm on both colored and nonlinear input with large dynamics. Despite its simplicity, the considered algorithm proves suitable for adaptive filtering of nonlinear and nonstationary signals. |
| doi_str_mv | 10.1109/LSP.2002.803001 |
| format | article |
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This is based on the adaptive amplitude backpropagation (AABP) algorithm for large-scale neural networks. The amplitude of the nonlinear activation function is made gradient adaptive to give the adaptive amplitude nonlinear gradient descent (AANGD) algorithm, making the AANGD suitable for processing nonlinear and nonstationary input signals with a large dynamical range. Experimental results show the AANGD algorithm outperforming the standard NGD algorithm on both colored and nonlinear input with large dynamics. Despite its simplicity, the considered algorithm proves suitable for adaptive filtering of nonlinear and nonstationary signals.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2002.803001</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive algorithms ; Adaptive filters ; Algorithms ; Amplitudes ; Backpropagation algorithms ; Biomedical signal processing ; Constraint optimization ; Descent ; Filtering algorithms ; Finite impulse response filter ; Impulse response ; Large-scale systems ; Neural networks ; Nonlinearity ; Signal processing ; Signal processing algorithms</subject><ispartof>IEEE signal processing letters, 2002-08, Vol.9 (8), p.253-255</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Despite its simplicity, the considered algorithm proves suitable for adaptive filtering of nonlinear and nonstationary signals.</description><subject>Adaptive algorithms</subject><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Amplitudes</subject><subject>Backpropagation algorithms</subject><subject>Biomedical signal processing</subject><subject>Constraint optimization</subject><subject>Descent</subject><subject>Filtering algorithms</subject><subject>Finite impulse response filter</subject><subject>Impulse response</subject><subject>Large-scale systems</subject><subject>Neural networks</subject><subject>Nonlinearity</subject><subject>Signal processing</subject><subject>Signal processing algorithms</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><recordid>eNqF0TFPwzAQBeAIgUQpzAwsFgNiSXsXx409oopCpQoQ0Nly0wt1lSbFdkD996QqCMQA093wvZNOL4pOEXqIoPqTp4deApD0JHAA3Is6KISMEz7A_XaHDGKlQB5GR94vAUCiFJ1oeldXpa3IODYaPzIzN-tg34gVtgzkPHu3YcEMe3FmbqkK38Cs1qUNzZyYrVhYEKu-DtmwOY4OClN6Ovmc3Wg6un4e3saT-5vx8GoS51xiiCk3kjgmhTLASaFShMbwQV6AzNMknYkZKTJizgs545imCZfGpDm0BCjLeTe62N1du_q1IR_0yvqcytJUVDdeJzIVHBD_h9kAhJRbePknxEGGXILIVEvPf9Fl3biq_VdLmXKhALeov0O5q713VOi1syvjNhpBb3vTbW9625ve9dYmznYJS0Q_NE-VQv4BhuWTEQ</recordid><startdate>20020801</startdate><enddate>20020801</enddate><creator>Hanna, A.I.</creator><creator>Mandic, D.P.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE signal processing letters, 2002-08, Vol.9 (8), p.253-255 |
| issn | 1070-9908 1558-2361 |
| language | eng |
| recordid | cdi_proquest_journals_884359019 |
| source | IEEE Electronic Library (IEL) Journals |
| subjects | Adaptive algorithms Adaptive filters Algorithms Amplitudes Backpropagation algorithms Biomedical signal processing Constraint optimization Descent Filtering algorithms Finite impulse response filter Impulse response Large-scale systems Neural networks Nonlinearity Signal processing Signal processing algorithms |
| title | Nonlinear FIR adaptive filters with a gradient adaptive amplitude in the nonlinearity |
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