Fuzzy Min-Max Neural Network With Fuzzy Lattice Inclusion Measure for Agricultural Circular Economy Region Division in Heilongjiang Province in China

Applying circular economy into agriculture is one of the most efficient methods to implement agricultural sustainable development. Designing different circular economy modes according to local characteristics of various regions can be more efficient. This paper focuses on agricultural circular econo...

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Main Authors: Xiangyan Meng, Muyan Liu, Meihong Wang, Jing Wang, Qiufeng Wu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9006825/
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spelling doaj-5d6d4aefc68349f7b4655c400aa76f822021-03-30T02:33:16ZengIEEEIEEE Access2169-35362020-01-018361203613010.1109/ACCESS.2020.29755619006825Fuzzy Min-Max Neural Network With Fuzzy Lattice Inclusion Measure for Agricultural Circular Economy Region Division in Heilongjiang Province in ChinaXiangyan Meng0https://orcid.org/0000-0002-4020-6906Muyan Liu1Meihong Wang2Jing Wang3Qiufeng Wu4https://orcid.org/0000-0002-4787-2549College of Arts and Sciences, Northeast Agricultural University, Harbin, ChinaCollege of Engineering, Northeast Agricultural University, Harbin, ChinaCollege of Arts and Sciences, Northeast Agricultural University, Harbin, ChinaCollege of Arts and Sciences, Northeast Agricultural University, Harbin, ChinaCollege of Arts and Sciences, Northeast Agricultural University, Harbin, ChinaApplying circular economy into agriculture is one of the most efficient methods to implement agricultural sustainable development. Designing different circular economy modes according to local characteristics of various regions can be more efficient. This paper focuses on agricultural circular economy region division in Heilongjiang province in China. First, the specific index system has been constructed. Then, according to these indexes to divide the regions via a novel but more efficient clustering method called Improved Fuzzy min-max neural network with fuzzy lattice inclusion measure (FL-IFMM) proposed in this paper. Heilongjiang province was divided into four agricultural circular economy regions which are respectively Farming(based on grain crops)-animal husbandry dominant region, Farming(based on rice) - animal husbandry dominant region, Vegetable and edible fungi - melons and fruits - animal husbandry dominant region, and Farming - Forestry - Animal husbandry dominant region. Finally, the circular economy modes fitting each region and some detailed policy suggestions have been proposed to help promote agricultural sustainable development in Heilongjiang province.https://ieeexplore.ieee.org/document/9006825/Agricultural circular economyclusteringfuzzy min-max neural networkfuzzy lattice inclusion measureregion division
collection DOAJ
language English
format Article
sources DOAJ
author Xiangyan Meng
Muyan Liu
Meihong Wang
Jing Wang
Qiufeng Wu
spellingShingle Xiangyan Meng
Muyan Liu
Meihong Wang
Jing Wang
Qiufeng Wu
Fuzzy Min-Max Neural Network With Fuzzy Lattice Inclusion Measure for Agricultural Circular Economy Region Division in Heilongjiang Province in China
IEEE Access
Agricultural circular economy
clustering
fuzzy min-max neural network
fuzzy lattice inclusion measure
region division
author_facet Xiangyan Meng
Muyan Liu
Meihong Wang
Jing Wang
Qiufeng Wu
author_sort Xiangyan Meng
title Fuzzy Min-Max Neural Network With Fuzzy Lattice Inclusion Measure for Agricultural Circular Economy Region Division in Heilongjiang Province in China
title_short Fuzzy Min-Max Neural Network With Fuzzy Lattice Inclusion Measure for Agricultural Circular Economy Region Division in Heilongjiang Province in China
title_full Fuzzy Min-Max Neural Network With Fuzzy Lattice Inclusion Measure for Agricultural Circular Economy Region Division in Heilongjiang Province in China
title_fullStr Fuzzy Min-Max Neural Network With Fuzzy Lattice Inclusion Measure for Agricultural Circular Economy Region Division in Heilongjiang Province in China
title_full_unstemmed Fuzzy Min-Max Neural Network With Fuzzy Lattice Inclusion Measure for Agricultural Circular Economy Region Division in Heilongjiang Province in China
title_sort fuzzy min-max neural network with fuzzy lattice inclusion measure for agricultural circular economy region division in heilongjiang province in china
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Applying circular economy into agriculture is one of the most efficient methods to implement agricultural sustainable development. Designing different circular economy modes according to local characteristics of various regions can be more efficient. This paper focuses on agricultural circular economy region division in Heilongjiang province in China. First, the specific index system has been constructed. Then, according to these indexes to divide the regions via a novel but more efficient clustering method called Improved Fuzzy min-max neural network with fuzzy lattice inclusion measure (FL-IFMM) proposed in this paper. Heilongjiang province was divided into four agricultural circular economy regions which are respectively Farming(based on grain crops)-animal husbandry dominant region, Farming(based on rice) - animal husbandry dominant region, Vegetable and edible fungi - melons and fruits - animal husbandry dominant region, and Farming - Forestry - Animal husbandry dominant region. Finally, the circular economy modes fitting each region and some detailed policy suggestions have been proposed to help promote agricultural sustainable development in Heilongjiang province.
topic Agricultural circular economy
clustering
fuzzy min-max neural network
fuzzy lattice inclusion measure
region division
url https://ieeexplore.ieee.org/document/9006825/
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