Home
Giới thiệu
Tài khoản
Đăng nhập
Quên mật khẩu
Đổi mật khẩu
Đăng ký tạo tài khoản
Liệt kê
Công trình khoa học
Bài báo trong nước
Bài báo quốc tế
Sách và giáo trình
Thống kê
Công trình khoa học
Bài báo khoa học
Sách và giáo trình
Giáo sư
Phó giáo sư
Tiến sĩ
Thạc sĩ
Lĩnh vực nghiên cứu
Tìm kiếm
Cá nhân
Nội dung
Góp ý
Hiệu chỉnh lý lịch
Thông tin chung
English
Đề tài NC khoa học
Bài báo, báo cáo khoa học
Hướng dẫn Sau đại học
Sách và giáo trình
Các học phần và môn giảng dạy
Giải thưởng khoa học, Phát minh, sáng chế
Khen thưởng
Thông tin khác
Tài liệu tham khảo
Hiệu chỉnh
Số người truy cập: 108,400,168
An Improved Nonlinear Grey Bernoulli Model Combined with Fourier series
Tác giả hoặc Nhóm tác giả:
Chia-Nan, Wang;
Van-Thanh, Phan
Nơi đăng:
Mathematical Problems in Engineering;
S
ố:
Article ID 740272;
Từ->đến trang
: 1-7;
Năm:
2015
Lĩnh vực:
Kinh tế;
Loại:
Bài báo khoa học;
Thể loại:
Quốc tế
TÓM TẮT
Grey forecasting is a dynamic forecasting model and has been widely used in various fields. In recent years, many scholars have proposed new procedures or new models to improve the precision accuracy of grey forecasting for the fluctuating data sets. However, the prediction accuracy of the grey forecasting models existing may not be always satisfactory in different scenario. For example, the data are highly fluctuating are with lots of noise. In order to deal with this issue, a Fourier Nonlinear Grey Bernoulli Model (1, 1) (abbreviated as F-NGBM (1, 1)) is proposed to enhance the forecasting performance. The proposed model was established by
using Fourier series to modify the residual errors of Nonlinear Grey Bernoulli Model (1, 1) (abbreviated as (NGBM (1, 1)). To verify the effectiveness of the proposed model, fluctuation data of the numerical example in Wang et al.’s paper (Wang et al. 2011) and practical application are used. Both of these simulation results demonstrate that the proposed model could forecast more precisely than several different kinds of grey forecasting models. For future direction, this proposed model can be applied to forecast the performance with the high fluctuation data in the different industries.
ABSTRACT
Grey forecasting is a dynamic forecasting model and has been widely used in various fields. In recent years, many scholars have proposed new procedures or new models to improve the precision accuracy of grey forecasting for the fluctuating data sets. However, the prediction accuracy of the grey forecasting models existing may not be always satisfactory in different scenario. For example, the data are highly fluctuating are with lots of noise. In order to deal with this issue, a Fourier Nonlinear Grey Bernoulli Model (1, 1) (abbreviated as F-NGBM (1, 1)) is proposed to enhance the forecasting performance. The proposed model was established by
using Fourier series to modify the residual errors of Nonlinear Grey Bernoulli Model (1, 1) (abbreviated as (NGBM (1, 1)). To verify the effectiveness of the proposed model, fluctuation data of the numerical example in Wang et al.’s paper (Wang et al. 2011) and practical application are used. Both of these simulation results demonstrate that the proposed model could forecast more precisely than several different kinds of grey forecasting models. For future direction, this proposed model can be applied to forecast the performance with the high fluctuation data in the different industries.
© Đại học Đà Nẵng
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: dhdn@ac.udn.vn