Identification of the non-linear systems using internal recurrent neural networks
In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitatea Dunarea de Jos
2006-12-01
|
| Series: | Analele Universităţii "Dunărea de Jos" Galaţi: Fascicula III, Electrotehnică, Electronică, Automatică, Informatică |
| Subjects: | |
| Online Access: | http://www.ann.ugal.ro/eeai/archives/2006/Lucrare-13-Puscasu.pdf |
| _version_ | 1818778441904816128 |
|---|---|
| author | Bogdan CODRES Gheorghe PUSCASU Alexandru STANCU |
| author_facet | Bogdan CODRES Gheorghe PUSCASU Alexandru STANCU |
| author_sort | Bogdan CODRES |
| collection | DOAJ |
| description | In the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks. |
| first_indexed | 2024-12-18T11:44:47Z |
| format | Article |
| id | doaj.art-654f7e0f027b49cb88b406bb865fe669 |
| institution | Directory Open Access Journal |
| issn | 1221-454X |
| language | English |
| last_indexed | 2024-12-18T11:44:47Z |
| publishDate | 2006-12-01 |
| publisher | Universitatea Dunarea de Jos |
| record_format | Article |
| series | Analele Universităţii "Dunărea de Jos" Galaţi: Fascicula III, Electrotehnică, Electronică, Automatică, Informatică |
| spelling | doaj.art-654f7e0f027b49cb88b406bb865fe6692022-12-21T21:09:18ZengUniversitatea Dunarea de JosAnalele Universităţii "Dunărea de Jos" Galaţi: Fascicula III, Electrotehnică, Electronică, Automatică, Informatică1221-454X2006-12-01200617481Identification of the non-linear systems using internal recurrent neural networksBogdan CODRESGheorghe PUSCASUAlexandru STANCUIn the past years utilization of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, tolerance to noise, resistance to partial destruction, parallel processing. Another major advantage of neural networks is that they allow us to obtain the model of the investigated system, systems that is not necessarily to be linear. In fact, the true value of neural networks is seen in the case of identification and control of nonlinear systems. In this paper there are presented some identification techniques using neural networks.http://www.ann.ugal.ro/eeai/archives/2006/Lucrare-13-Puscasu.pdfIdentificationrecurrent neural networkstraining |
| spellingShingle | Bogdan CODRES Gheorghe PUSCASU Alexandru STANCU Identification of the non-linear systems using internal recurrent neural networks Analele Universităţii "Dunărea de Jos" Galaţi: Fascicula III, Electrotehnică, Electronică, Automatică, Informatică Identification recurrent neural networks training |
| title | Identification of the non-linear systems using internal recurrent neural networks |
| title_full | Identification of the non-linear systems using internal recurrent neural networks |
| title_fullStr | Identification of the non-linear systems using internal recurrent neural networks |
| title_full_unstemmed | Identification of the non-linear systems using internal recurrent neural networks |
| title_short | Identification of the non-linear systems using internal recurrent neural networks |
| title_sort | identification of the non linear systems using internal recurrent neural networks |
| topic | Identification recurrent neural networks training |
| url | http://www.ann.ugal.ro/eeai/archives/2006/Lucrare-13-Puscasu.pdf |
| work_keys_str_mv | AT bogdancodres identificationofthenonlinearsystemsusinginternalrecurrentneuralnetworks AT gheorghepuscasu identificationofthenonlinearsystemsusinginternalrecurrentneuralnetworks AT alexandrustancu identificationofthenonlinearsystemsusinginternalrecurrentneuralnetworks |