326 resultados para INFERÊNCIA


Relevância:

10.00% 10.00%

Publicador:

Resumo:

The aim of this study was to assess the occurrence of genotype-environment interaction, as well as its effects on the magnitude of genetic parameters and the classification of Nellore breeding bulls for the trait adjusted weight at 205 days (W205) on Southern Brazil. The components of (co)variance were estimated by Bayesian inference, using a linear-linear animal model in a bi-trait analysis. The proposed model for the analyses considers as random the direct additive genetic and maternal effects and residual effects, and as fixed effects the contemporary groups, sex, season of birth and weighing, and calving age as covariable (linear and quadratic effects). The a posteriori mean estimates of the direct heritabilities for W205 in the three States varied from 0.24 in Paraná (PR) to 0.34 in Santa Catarina (SC). The estimates of maternal heritability varied from 0.23 in SC and Rio Grande do Sul (RS) to 0.28 in PR. The a posteriori mean distributions of the genetic correlation varied from 0.52 between SC and RS, to 0.84 between PR and RS, suggesting that the best breeding bulls in SC are not the same as in RS.

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Pós-graduação em Direito - FCHS

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Pós-graduação em Ciências Ambientais - Sorocaba

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Relevância:

10.00% 10.00%

Publicador:

Resumo:

This research aimed to develop a Fuzzy inference based on expert system to help preventing lameness in dairy cattle. Hoof length, nutritional parameters and floor material properties (roughness) were used to build the Fuzzy inference system. The expert system architecture was defined using Unified Modelling Language (UML). Data were collected in a commercial dairy herd using two different subgroups (H-1 and H-2), in order to validate the Fuzzy inference functions. The numbers of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) responses were used to build the classifier system up, after an established gold standard comparison. A Lesion Incidence Possibility (LIP) developed function indicates the chances of a cow becoming lame. The obtained lameness percentage in H-1 and H-2 was 8.40% and 1.77%, respectively. The system estimated a Lesion Incidence Possibility (LIP) of 5.00% and 2.00% in H-1 and H-2, respectively. The system simulation presented 3.40% difference from real cattle lameness data for H-1, while for H-2, it was 0.23%; indicating the system efficiency in decision-making.

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Relevância:

10.00% 10.00%

Publicador:

Resumo:

The objective of this work is to determine the membership functions for the construction of a fuzzy controller to evaluate the energy situation of the company with respect to load and power factors. The energy assessment of a company is performed by technicians and experts based on the indices of load and power factors, and analysis of the machines used in production processes. This assessment is conducted periodically to detect whether the procedures performed by employees in relation to how of use electricity energy are correct. With a fuzzy controller, this performed can be done by machines. The construction of a fuzzy controller is initially characterized by the definition of input and output variables, and their associated membership functions. We also need to define a method of inference and a processor output. Finally, you need the help of technicians and experts to build a rule base, consisting of answers that provide these professionals in function of characteristics of the input variables. The controller proposed in this paper has as input variables load and power factors, and output the company situation. Their membership functions representing fuzzy sets called by linguistic qualities, as “VERY BAD” and “GOOD”. With the method of inference Mandani and the processor to exit from the Center of Area chosen, the structure of a fuzzy controller is established, simply by the choice by technicians and experts of the field energy to determine a set of rules appropriate for the chosen company. Thus, the interpretation of load and power factors by software comes to meeting the need of creating a single index that indicates an overall basis (rational and efficient) as the energy is being used.

Relevância:

10.00% 10.00%

Publicador:

Resumo:

Pós-graduação em Educação - FCT