论文标题
使用人工神经网络的混合模型对联合收割机的性能分析粒子群优化
Performance Analysis of Combine Harvester using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization
论文作者
论文摘要
人工智能在调整工业机器的参数以进行最佳性能的新颖应用以快速发展。调整联合收割机并改善机器性能可以极大地最大程度地减少收割过程中的废物，并且对机器维护也有益。文献包括几种软计算，机器学习和优化方法，这些方法用于建模各种农作物的收割者的功能。由于问题的复杂性，最近提出了机器学习方法来预测最佳性能，并有希望的结果。在本文中，通过提出一种基于与粒子群优化（ANN-PSO）集成的人工神经网络的新型混合机学习模型，提出了共同联合收割机的性能分析。机器学习方法与软计算技术的杂交最近显示出令人鼓舞的结果，以提高联合收割机的性能。这项研究旨在通过以更高的精度提供更稳定的模型来进一步改善结果。
Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy.
