Nadai, L.; Felde, I.; Ardabili, S.; Mesri Gundoshmian, T.; Pinter, G.; Mosavi, A. Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization. Preprints2020, 2020020336. https://doi.org/10.20944/preprints202002.0336.v1
APA Style
Nadai, L., Felde, I., Ardabili, S., Mesri Gundoshmian, T., Pinter, G., & Mosavi, A. (2020). Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization. Preprints. https://doi.org/10.20944/preprints202002.0336.v1
Chicago/Turabian Style
Nadai, L., Gergo Pinter and Amir Mosavi. 2020 "Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization" Preprints. https://doi.org/10.20944/preprints202002.0336.v1
Abstract
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.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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.
