论文标题
神经网络算法及其在超临界提取中的应用
A review of neural network algorithms and their applications in supercritical extraction
论文作者
论文摘要
神经网络通过模拟人脑的某些机制实现多参数优化和控制。它可以用于许多领域，例如信号处理，智能驾驶，最佳组合，车辆异常检测和化学过程优化控制。超临界提取是一种新型的高效化学分离过程，主要用于天然物质的分离和纯化。有许多影响因素。神经网络模型可以快速优化过程参数，并在不同的过程条件下预测实验结果。了解实验的内部定律并确定最佳实验条件是有帮助的。本文简要介绍了神经网络和超临界提取的基本概念和研究进度，并总结了神经网络算法在超临界提取中的应用，旨在为行业技术的发展和创新提供参考。
Neural network realizes multi-parameter optimization and control by simulating certain mechanisms of the human brain. It can be used in many fields such as signal processing, intelligent driving, optimal combination, vehicle abnormality detection, and chemical process optimization control. Supercritical extraction is a new type of high-efficiency chemical separation process, which is mainly used in the separation and purification of natural substances. There are many influencing factors. The neural network model can quickly optimize the process parameters and predict the experimental results under different process conditions. It is helpful to understand the inner law of the experiment and determine the optimal experimental conditions. This paper briefly describes the basic concepts and research progress of neural networks and supercritical extraction, and summarizes the application of neural network algorithms in supercritical extraction, aiming to provide reference for the development and innovation of industry technology.
