一種啟發(fā)式變階直覺(jué)模糊時(shí)間序列預(yù)測(cè)模型

王亞男; 雷英杰; 王毅; 鄭寇全

doi:10.11999/JEIT160013

一種啟發(fā)式變階直覺(jué)模糊時(shí)間序列預(yù)測(cè)模型

doi: 10.11999/JEIT160013

1.
(空軍工程大學(xué)防空反導(dǎo)學(xué)院西安 710051) ②(西安通信學(xué)院西安 710106)

基金項(xiàng)目:

國(guó)家自然科學(xué)青年基金項(xiàng)目(61402517)

計(jì)量
- 文章訪問(wèn)數(shù): 1239
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- PDF下載量: 415
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-01-04
- 修回日期: 2016-05-26
- 刊出日期: 2016-11-19

A Heuristic Adaptive-order Intuitionistic Fuzzy Time Series Forecasting Model

1.
(Air and Missile Defense College, Air Force Engineering University, Xi&rsquo
2.
(Xi&rsquo

Funds:

The National Natural Science Foundation of China (61402517)

摘要

摘要: 論文針對(duì)已有高階模糊時(shí)間序列模型在預(yù)測(cè)精度和預(yù)測(cè)范圍上的限制，結(jié)合直覺(jué)模糊集理論，提出一種啟發(fā)式變階直覺(jué)模糊時(shí)間序列預(yù)測(cè)模型。模型首先應(yīng)用直接模糊聚類算法對(duì)論域進(jìn)行非等分劃分；然后，針對(duì)直覺(jué)模糊時(shí)間序列的數(shù)據(jù)特性，改進(jìn)現(xiàn)有直覺(jué)模糊集隸屬度和非隸屬度函數(shù)的建立方法；最后，采用階數(shù)隨序列實(shí)時(shí)變化的高階預(yù)測(cè)規(guī)則進(jìn)行預(yù)測(cè)，并將歷史數(shù)據(jù)發(fā)展趨勢(shì)的啟發(fā)知識(shí)引入解模糊過(guò)程，使模型的預(yù)測(cè)范圍得到擴(kuò)展。在Alabama大學(xué)入學(xué)人數(shù)和北京市日均氣溫兩組數(shù)據(jù)集上分別與典型方法進(jìn)行對(duì)比實(shí)驗(yàn)，結(jié)果表明該模型有效克服了傳統(tǒng)模型的缺點(diǎn)，擁有較高的預(yù)測(cè)精度，證明了模型的有效性和優(yōu)越性。
- 直覺(jué)模糊集 /
- 時(shí)間序列預(yù)測(cè) /
- 啟發(fā)式 /
- 變階
Abstract: Considering that the existing high-order models have limitations in forecast range and accuracy, a heuristic adaptive-order intuitionistic fuzzy time series forecasting model is built with the combination of the intuitionistic fuzzy sets theory. In this model, a direct fuzzy clustering algorithm is used to partition the universe of discourse into unequal intervals. The traditional method of ascertaining the membership and non-membership functions of intuitionistic fuzzy set are also modified to fit the intuitionistic fuzzy time series data. On these basis, variable high-order forecasting rules are established and the prior knowledge of tendency is used in defuzzification to extend the forecasting range. At last, contrast experiments on the enrollments of the University of Alabama and the daily average temperature of Beijing are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.
- Intuitionistic fuzzy set /
- Time series forecast /
- Heuristic /
- Adaptive order

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