{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:28:17Z","timestamp":1740148097105,"version":"3.37.3"},"reference-count":13,"publisher":"Wiley","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["SIP20160382"],"award-info":[{"award-number":["SIP20160382"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003069","name":"Instituto Polit\u00e9cnico Nacional","doi-asserted-by":"publisher","award":["SIP20160382"],"award-info":[{"award-number":["SIP20160382"]}],"id":[{"id":"10.13039\/501100003069","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>A<\/mml:mi><\/mml:mrow><\/mml:math>and the proper gain<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mi>K<\/mml:mi><\/mml:mrow><\/mml:math>into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system.<\/jats:p>","DOI":"10.1155\/2016\/1690924","type":"journal-article","created":{"date-parts":[[2016,6,5]],"date-time":"2016-06-05T21:01:00Z","timestamp":1465160460000},"page":"1-10","source":"Crossref","is-referenced-by-count":1,"title":["Neural Net Gains Estimation Based on an Equivalent Model"],"prefix":"10.1155","volume":"2016","author":[{"given":"Karen Alicia","family":"Aguilar Cruz","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n, Instituto Polit\u00e9cnico Nacional (CIC-IPN), Avenida Juan de Dios B\u00e1tiz, Esq. Miguel Oth\u00f3n de Mendiz\u00e1bal, Col. 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