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Araújo Júnior JM, Linhares LL, Araújo FM, Almeida OM. Fuzzy wavelet neural networks applied as inferential sensors of neonatal incubator dynamics. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-190129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wavelet neural network (FWNN) can be regarded as a prominent technique, consisting of the combination of wavelet neural network (WNN) and adaptive network-based fuzzy inference system (ANFIS). This work proposes the use of FWNN to infer the temperature and humidity values inside the incubator in order to certify the equipment operation. Results obtained with the analyzed neural system have shown the generalization and inference capacities of FWNNs, thus allowing their application to practical tasks aiming to increase the efficiency of incubators.
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Affiliation(s)
- José M. Araújo Júnior
- Department of Electrical Engineering, Federal University of Piauí (UFPI), Teresina, PI, Brazil
| | - Leandro L.S. Linhares
- Federal Institute of Education, Science and Technology of Paraíba (IFPB), Cajazeiras, PB, Brazil
| | - Fábio M.U. Araújo
- Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil
| | - Otacílio M. Almeida
- Department of Electrical Engineering, Federal University of Piauí (UFPI), Teresina, PI, Brazil
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Goudarzi S, Khodabakhshi MB, Moradi MH. Interactively recurrent fuzzy functions with multi objective learning and its application to chaotic time series prediction. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2016. [DOI: 10.3233/ifs-151839] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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