Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network
Artificial neural networks (ANNs) have been used over the last few decades to perform tasks by learning with comparisons. Fitting input-output models, system identification, control, and pattern recognition are some fields for ANN applications. However, problems involving uncertain situations could...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9430548/ |
| _version_ | 1818573945234784256 |
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| author | Arnaldo de Carvalho Joao Francisco Justo Bruno Augusto Angelico Alexandre Manicoba de Oliveira Joao Inacio da Silva Filho |
| author_facet | Arnaldo de Carvalho Joao Francisco Justo Bruno Augusto Angelico Alexandre Manicoba de Oliveira Joao Inacio da Silva Filho |
| author_sort | Arnaldo de Carvalho |
| collection | DOAJ |
| description | Artificial neural networks (ANNs) have been used over the last few decades to perform tasks by learning with comparisons. Fitting input-output models, system identification, control, and pattern recognition are some fields for ANN applications. However, problems involving uncertain situations could be challenging for them. The family of paraconsistent logics (PL) is a powerful tool that can deal with uncertainty and contradictory information, so getting attention from researchers for its implications and applications in artificial intelligence. This investigation describes a novel activation function reasoned on the paraconsistent annotated logic by two-value annotations (PAL2v) rules, a variation of PL, allowing the design of a new paraconsistent neural net (PNN), applied in model identification for control (I4C) of a closed-loop rotary inverted pendulum (RIP) system. |
| first_indexed | 2024-12-15T00:19:42Z |
| format | Article |
| id | doaj.art-08677a6647a7430e9f9737ece43f7cd6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-15T00:19:42Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-08677a6647a7430e9f9737ece43f7cd62022-12-21T22:42:20ZengIEEEIEEE Access2169-35362021-01-019741557416710.1109/ACCESS.2021.30801769430548Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural NetworkArnaldo de Carvalho0https://orcid.org/0000-0002-3417-0062Joao Francisco Justo1https://orcid.org/0000-0003-1948-7835Bruno Augusto Angelico2https://orcid.org/0000-0002-2748-5365Alexandre Manicoba de Oliveira3https://orcid.org/0000-0002-7493-7117Joao Inacio da Silva Filho4https://orcid.org/0000-0001-9715-8928Federal Institute of Education Science and Technology of Sao Paulo (IFSP), Cubatão, BrazilEscola Politécnica, Universidade de São Paulo, São Paulo, BrazilEscola Politécnica, Universidade de São Paulo, São Paulo, BrazilFederal Institute of Education Science and Technology of Sao Paulo (IFSP), Cubatão, BrazilDepartment of Electronic Engineering and Computation, Laboratory of Applied Paraconsistent Logic, Santa Cecília University (UNISANTA), Santos, BrazilArtificial neural networks (ANNs) have been used over the last few decades to perform tasks by learning with comparisons. Fitting input-output models, system identification, control, and pattern recognition are some fields for ANN applications. However, problems involving uncertain situations could be challenging for them. The family of paraconsistent logics (PL) is a powerful tool that can deal with uncertainty and contradictory information, so getting attention from researchers for its implications and applications in artificial intelligence. This investigation describes a novel activation function reasoned on the paraconsistent annotated logic by two-value annotations (PAL2v) rules, a variation of PL, allowing the design of a new paraconsistent neural net (PNN), applied in model identification for control (I4C) of a closed-loop rotary inverted pendulum (RIP) system.https://ieeexplore.ieee.org/document/9430548/Paraconsistent logicneural netmodel identificationpattern analysisrotary inverted pendulum |
| spellingShingle | Arnaldo de Carvalho Joao Francisco Justo Bruno Augusto Angelico Alexandre Manicoba de Oliveira Joao Inacio da Silva Filho Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network IEEE Access Paraconsistent logic neural net model identification pattern analysis rotary inverted pendulum |
| title | Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network |
| title_full | Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network |
| title_fullStr | Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network |
| title_full_unstemmed | Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network |
| title_short | Rotary Inverted Pendulum Identification for Control by Paraconsistent Neural Network |
| title_sort | rotary inverted pendulum identification for control by paraconsistent neural network |
| topic | Paraconsistent logic neural net model identification pattern analysis rotary inverted pendulum |
| url | https://ieeexplore.ieee.org/document/9430548/ |
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