模糊逻辑
神经模糊
计算机科学
自适应神经模糊推理系统
模糊控制系统
控制器(灌溉)
去模糊化
人工智能
控制工程
模糊聚类
模糊集运算
控制理论(社会学)
模糊数
数据挖掘
工程类
模糊集
控制(管理)
生物
农学
作者
Soleiman Hosseinpour,Alex Martynenko
标识
DOI:10.1080/07373937.2022.2119996
摘要
A systematic approach to the design of an adaptive fuzzy logic controller (AFLC) for intelligent drying with a computer vision system (CVS) in a feedback loop is proposed. Developed AFLC is based on an artificial neural network (ANN), geno-fuzzy algorithm, and multi-objective fuzzy cost function. Fuzzy sets for the moisture content and product quality are automatically generated by using principal component analysis (PCA) and fuzzy clustering. In addition, the concept of fuzzy time is introduced to optimize the duration of each control step. The fuzzy rule base for the controller was constructed through a two-stage process of (i) warming-up based on simulation and optimization (offline) and (ii) fine-tuning during real-time drying (online). The application of AFLC for shrimp drying showed advantages of the unsupervised fuzzy logic control, such as decreased drying time, less quality degradation, and smaller energy consumption.
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