Multiscale RBF Neural Network for Forecasting of Monthly Hake Catches off Southern Chile



Document title: Multiscale RBF Neural Network for Forecasting of Monthly Hake Catches off Southern Chile
Journal: Polibits
Database: PERIÓDICA
System number: 000374540
ISSN: 1870-9044
Authors: 1
2
1
Institutions: 1Pontificia Universidad Católica de Valparaíso, Escuela de Ingeniería de Computación, Valparaíso. Chile
2Universidad Nacional de Chimborazo, Escuela de Ingeniería de Computación, Riobamba, Chimborazo. Ecuador
Year:
Season: Jul-Dic
Number: 48
Pages: 47-53
Country: México
Language: Inglés
Document type: Artículo
Approach: Aplicado, descriptivo
English abstract We present a forecasting strategy based on stationary wavelet transform combined with radial basis function (RBF) neural network to improve the accuracy of 3-month-ahead hake catches forecasting of the fisheries industry in the central southern Chile. The general idea of the proposed forecasting model is to decompose the raw data set into an annual cycle component and an inter-annual component by using 3-levels stationary wavelet decomposition. The components are independently predicted using an autoregressive RBF neural network model. The RBF neural network model is composed of linear and nonlinear weights, which are estimates using the separable nonlinear least squares method. Consequently, the proposed forecaster is the co-addition of two predicted components. We demonstrate the utility of the proposed model on hake catches data set for monthly periods from 1963 to 2008. Experimental results on hake catches data show that the autoregressive RBF neural network model is effective for 3-month-ahead forecasting
Disciplines: Ciencias de la computación,
Medicina veterinaria y zootecnia
Keyword: Pesca,
Redes neuronales,
Modelos de predicción,
Captura,
Merluza,
Manejo pesquero,
Mínimos cuadrados no-lineales
Keyword: Computer science,
Veterinary medicine and animal husbandry,
Fisheries,
Fishing,
Neural networks,
Forecast models,
Catch,
Hake,
Fishery management,
Nonlinear least squares
Full text: Texto completo (Ver HTML)