title: Applying fuzzy logic to estimate the parameters of the length-weight relationship.
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 also indicate that it is possible to find consistent estimates of the parameters directly from the centers of mass of each cluster. However, the most important result is the intervals obtained with the fuzzy inference system.
authors.affiliation: Department of Mathematics, Universidade Federal do Amazonas, Universidade Federal do Amazonas, Manaus AM , Brazil, Department of Mathematics, Universidade Federal do Amazonas - UFAM, Av. General Rodrigo Otávio Jordão Ramos, 3000, Campus Universitário, CEP 69077-000, Manaus, AM, Brazil.
authors.forename: S D
authors.initials: SD
authors.lastname: Bitar
authors.affiliation: Department of Fisheries Sciences, Universidade Federal do Amazonas, Universidade Federal do Amazonas, Manaus AM , Brazil, Department of Fisheries Sciences, Universidade Federal do Amazonas - UFAM, Av. General Rodrigo Otávio Jordão Ramos, 3000, Campus Universitário, CEP 69077-000, Manaus, AM, Brazil.
authors.forename: C P
authors.initials: CP
authors.lastname: Campos
authors.affiliation: Department of Fisheries Sciences, Universidade Federal do Amazonas, Universidade Federal do Amazonas, Manaus AM , Brazil, Department of Fisheries Sciences, Universidade Federal do Amazonas - UFAM, Av. General Rodrigo Otávio Jordão Ramos, 3000, Campus Universitário, CEP 69077-000, Manaus, AM, Brazil.
authors.forename: C E C
authors.initials: CE
authors.lastname: Freitas
mesh_terms: D000818:Animals; D001835:Body Weight; D017143:Fuzzy Logic; D008954:Models, Biological