期刊
IEEE ACCESS
卷 9, 期 -, 页码 74155-74167出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3080176
关键词
Neurons; Uncertainty; Lattices; Biological neural networks; Annotations; Control systems; Adaptation models; Paraconsistent logic; neural net; model identification; pattern analysis; rotary inverted pendulum
资金
- Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brazil (CAPES) [001]
Artificial neural networks have been widely used in various fields over the past few decades, but they may face challenges when dealing with uncertain situations. The family of paraconsistent logics, as a powerful tool for handling uncertainty and contradictory information, has attracted attention from researchers in the field of artificial intelligence.
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.
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