2016
DOI: 10.1590/1519-6984.20014
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Abstract: We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results a… Show more

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Cited by 14 publications
(10 citation statements)
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“…In this section are presented the results obtained after use LSM for determining every fuzzy system related to its corresponding column so that the union of all 9 fuzzy systems replace the AWG 13 show the comparison between the plot of real data in the AWG table and the obtained using the fuzzy system trained with LSM and the obtained matrixes 1... 9 A .…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In this section are presented the results obtained after use LSM for determining every fuzzy system related to its corresponding column so that the union of all 9 fuzzy systems replace the AWG 13 show the comparison between the plot of real data in the AWG table and the obtained using the fuzzy system trained with LSM and the obtained matrixes 1... 9 A .…”
Section: Resultsmentioning
confidence: 99%
“…Fuzzy inference systems have been spread in several areas since its very beginning in 1965, today there still appearing several applications because of its capability for mimic human reasoning using expert knowledge. There are several papers where fuzzy systems have been applied in the recent years, they have been used in image enhancement in [6], image compression analysis in [7], risk evaluation and periodization in industries in [8], estimating the length-weight relationship of fishes in [9], for failure analyzing in automobile industry in [10], in the assessment of power transformers in [11], among other several applications.…”
Section: Introductionmentioning
confidence: 99%
“…For the determination of the degree of membership fuzzy, the variables used in the system is the age and body mass index or BMI. In the fuzzy set [9] for age, there are three parts, namely fuzzy set young age, adulthood and old age with the following caption:…”
Section: Factor Of Activity Equation Informationmentioning
confidence: 99%
“…Implementation of soft computing protocols include techniques of fuzzy set theory, neural networks, probabilistic reasoning, rough sets, machine learning, and evolutionary computing (Zadeh, 1993;Oduguwa, Tiwari & Roy, 2005;Bello & Verdegay, 2012;Ibrahim, 2016;Al-Kaysi et al, 2017;Herrera-Viedma & López-Herrera, 2010). In this upsurge of nonconventional analytical tools, we can place adaptation of fuzzy logic procedures aimed to lessen parametric uncertainty effects in allometry (Schreer, 1997;Schwetter & Bertone, 2018;Bitar, Campos & Freitas, 2016;Echavarría-Heras et al, 2018a;Näther & Wälder, 2006;Dechnik-Vázquez et al, 2019).…”
Section: Introductionmentioning
confidence: 99%