Next Article in Journal
Oscillatory Behavior of Three Dimensional α-Fractional Delay Differential Systems
Previous Article in Journal
Relation Theoretic (Θ,R) Contraction Results with Applications to Nonlinear Matrix Equations
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(12), 768; https://doi.org/10.3390/sym10120768

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

1
Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110012, India
2
Division of Data Science, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
3
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
*
Author to whom correspondence should be addressed.
Received: 15 November 2018 / Revised: 29 November 2018 / Accepted: 30 November 2018 / Published: 18 December 2018
Full-Text   |   PDF [2069 KB, uploaded 18 December 2018]   |  

Abstract

Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER). View Full-Text
Keywords: wheat production prediction; fuzzy rules; time series; fuzzy regression wheat production prediction; fuzzy rules; time series; fuzzy regression
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Jain, R.; Jain, N.; Kapania, S.; Son, L.H. Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction. Symmetry 2018, 10, 768.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top