{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T10:44:23Z","timestamp":1648982663002},"reference-count":9,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2000,4]]},"abstract":"<jats:p> Nonlinear blind signal separation is an important but rather difficult problem. Any general nonlinear independent component analysis algorithm for such a problem should specify which solution it tries to find. Several recent neural networks for separating the post nonlinear blind mixtures are limited to the diagonal nonlinearity, where there is no cross-channel nonlinearity. In this paper, a new semi-parametric hybrid neural network is proposed to separate the post nonlinearly mixed blind signals where cross-channel disturbance is included. This hybrid network consists of two cascading modules, which are a neural nonlinear module for approximating the post nonlinearity and a linear module for separating the predicted linear blind mixtures. The nonlinear module is a semi-parametric expansion made up of two sub-networks, one of which is a linear model and the other of which is a three-layer perceptron. These two sub-networks together produce a \"weak\" nonlinear operator and can approach relatively strong nonlinearity by tuning parameters. A batch learning algorithm based on the entropy maximization and the gradient descent method is deduced. This model is successfully applied to a blind signal separation problem with two sources. Our simulation results indicate that this hybrid model can effectively approach the cross-channel post nonlinearity and achieve a good visual quality as well as a high signal-to-noise ratio in some cases. <\/jats:p>","DOI":"10.1142\/s0129065700000089","type":"journal-article","created":{"date-parts":[[2003,4,22]],"date-time":"2003-04-22T11:45:54Z","timestamp":1051011954000},"page":"79-93","source":"Crossref","is-referenced-by-count":1,"title":["A SEMI-PARAMETRIC HYBRID NEURAL MODEL FOR  NONLINEAR BLIND SIGNAL SEPARATION"],"prefix":"10.1142","volume":"10","author":[{"given":"HANCHUAN","family":"PENG","sequence":"first","affiliation":[{"name":"Center for Multimedia Signal Processing,  Department of Electronic &amp; Information Engineering,  The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China"},{"name":"Department of Biomedical Engineering,  Southeast University, Nanjing, 210096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZHERU","family":"CHI","sequence":"additional","affiliation":[{"name":"Center for Multimedia Signal Processing,  Department of Electronic &amp; Information Engineering,  The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"WANCHI","family":"SIU","sequence":"additional","affiliation":[{"name":"Center for Multimedia Signal Processing,  Department of Electronic &amp; Information Engineering,  The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2012,4,19]]},"reference":[{"key":"p_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(98)00140-3"},{"key":"p_3","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(05)80090-5"},{"key":"p_4","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(94)00108-X"},{"key":"p_9","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-1684(97)00196-5"},{"key":"p_10","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.7.1483"},{"key":"p_11","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1995.7.6.1129"},{"key":"p_15","doi-asserted-by":"publisher","DOI":"10.1109\/97.566704"},{"key":"p_16","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1684(94)90029-9"},{"key":"p_17","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065797000586"}],"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065700000089","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T21:09:53Z","timestamp":1565125793000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0129065700000089"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2000,4]]},"references-count":9,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2012,4,19]]},"published-print":{"date-parts":[[2000,4]]}},"alternative-id":["10.1142\/S0129065700000089"],"URL":"https:\/\/doi.org\/10.1142\/s0129065700000089","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2000,4]]}}}