Quality Evaluation of Ranunculaceae Essential Oil with the Artificial Neural Network

Abstract

The goal of this project is to use ANN to build a quality evaluation method for Ranunculaceae essential oils in order to assist businesses or individuals in setting up an automated essential oil quality evaluation system and reducing human resource consumption. Using computer software to evaluate the quality of Ranunculaceae essential oils, quantitatively evaluate product quality, and improve the automation of quality evaluation. The main research contents in this paper are as follows: (1) In-depth research on PSO and its development. The purpose of studying the PSO is to use PSO to replace gradient descent algorithm in BP network, so that the neural network model has a better network structure. This paper improves it on the basis of PSO. The basic improvement idea is to add a dynamic random position transformation to the particle, so that global optimization ability can be enhanced. The improved algorithm and the standard algorithm are compared and analyzed in the simulation experiments. The results verify that improved PSO has better optimization ability. (2) Replace training algorithm in the BP network with the IPSO and construct the BP network algorithm model of IPSO. The final implementation of the Ranunculaceae plant essential oil quality assessment model based on the IPSO-BP network was achieved by combining the Ranunculaceae plant essential oil quality evaluation index system with the Ranunculaceae plant essential oil quality evaluation index system. Finally, a simulation experiment was designed to compare the presentation of PSO-BP and IPSO-BP, when they were applied to the evaluation of Ranunculaceae vital oils. The experimental results show IPSO-BP has the highest estimate accuracy and the best performance.