人工神经网络
人工智能
粒子群优化
过程(计算)
机器学习
计算机科学
自适应神经模糊推理系统
支持向量机
差异进化
推论
随机森林
数据挖掘
模糊逻辑
模糊控制系统
操作系统
作者
Fulya Aydın Temel,Özge Cağcağ Yolcu,Nurdan Gamze Turan
标识
DOI:10.1016/j.biortech.2022.128539
摘要
Studies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, and managing complex variables. This review investigates the perspectives and challenges of ML and its important algorithms such as Artificial Neural Networks (ANNs), Random Forest (RF), Adaptive-network-based fuzzy inference systems (ANFIS), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs) used in the composting process. In addition, the individual shortcomings and inadequacies of the metrics, which were used as error or performance criteria in the studies, were emphasized. Except for a few studies, it was concluded that Artificial Intelligence (AI) algorithms such as Genetic algorithm (GA), Differential Evaluation Algorithm (DEA), and Particle Swarm Optimization (PSO) were not used in the optimization of the model parameters, but in the optimization of the parameters of the ML algorithms.
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