Consulta: subjectFacets:"Neuro-fuzzy"
Registros recuperados: 3
Data/hora: 12/06/2024 06:34:34

NEURO-FUZZY MODELING OF EYEBALL AND CREST TEMPERATURES IN EGG-LAYING HENS
Provedor de dados: 70
Autores: Lins,Ana C. de S. S.; Lourençoni,Dian; Yanagi Júnior,Tadayuki; Miranda,Isadora B.; Santos,Italo E. dos A.
ABSTRACT Considering the challenges faced by poultry farming, this study aimed to develop a neuro-fuzzy model to predict eyeball and crest temperatures of egg-laying hens based on environmental conditions (dry bulb temperature and relative humidity). To develop the models and simulations, Matlab’s Fuzzy Toolbox® (Anfisedit) was used. Different configurations were used for each of the several neuro-fuzzy models developed. Eyeball temperature (ET) and chicken crest temperature (CCT) were simulated from the developed neuro-fuzzy models, and the obtained results were validated with the variables collected experimentally with the aid of recorder sensors and an infrared thermographic camera. The proposed neuro-fuzzy models allow the accurate estimation of ET and...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Neuro-fuzzy; Thermography; Poultry farming; Simulation; Artificial intelligence.
Ano: 2021
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162021000100034

Fruit production forecasting by neuro-fuzzy techniques
Provedor de dados: 31
Autores: Atsalakis,   George S.; Atsalakis,   Ioanna G.
Neuro-fuzzy techniques are finding a practical application in many fields such as in model identification and forecasting of linear and non-linear systems. This paper presents a neuro-fuzzy model for forecasting the fruit production of some agriculture products (olives, lemons, oranges, cherries and pistachios). The model utilizes a time series of yearly data. The fruit forecasting is based on Adaptive Neural Fuzzy Inference System (ANFIS). ANFIS uses a combination of the least-squares method and the backprobagation gradient descent method to estimate the optimal food forecast parameters for each year. The results are compared to those of an Autoregressive (AR) model and an Autoregressive Moving Average model (ARMA).
Tipo: Conference Paper or Presentation
Palavras-chave: Fruit forecasting; Neuro-fuzzy; ANFIS; AR; ARMA; Forecasting; Fruit production; Agricultural Finance; Crop Production/Industries.
Ano: 2010
URL: http://purl.umn.edu/57680

Sistema computacional para auxílio ao diagnóstico em exames de tuberculose animal
Provedor de dados: 45
Autores: Gracioso,A.C.N.R.; Souza Filho,R.A.M.; Gonzaga,A.; Fernandez,F.J.R.
The results obtained in evaluating the efficiency of a Neuro-Fuzzy System NEFCLASS (Neuro-Fuzzy Classification) in image classification of cattle tuberculosis, based on its texture features extracted using the wavelet transform are presented. For testing, images of animal tissues diagnosed with tuberculosis were used, as provided by the Tuberculosis Laboratory at the Instituto Biológico de São Paulo. The results of this study can serve as a basis for developing systems for diagnosis aimed at reducing human effort, by automating all or parts of the classification of images, helping lab technicians to sort amongst different pathologies.
Tipo: Info:eu-repo/semantics/article
Palavras-chave: CBIR; Wavelets; Neuro-fuzzy; Tuberculosis.
Ano: 2013
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-09352013000200045