Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

Version 1 : Received: 22 February 2020 / Approved: 24 February 2020 / Online: 24 February 2020 (01:52:19 CET)

How to cite: 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. Preprints 2020, 2020020336 (doi: 10.20944/preprints202002.0336.v1). 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. Preprints 2020, 2020020336 (doi: 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.

Subject Areas

combine harvester; hybrid machine learning; artificial neural networks (ANN); particle swarm optimization (PSO); ANN-PSO

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