灰狼算法优化BP神经网络的股价预测
Stock Price Prediction of the BP Neural Network Optimzed by the Grey Wolf Optimizer
向朝菊1
作者信息
摘要
探讨使用灰狼算法改进BP神经网络的方法,旨在提高BP神经网络的训练效果和性能.首先,介绍了BP神经网络的基本原理和灰狼算法的基本概念.然后,将灰狼算法应用于BP神经网络的权重和偏置值的优化过程中,通过调整这些参数来降低误差函数,从而提高网络的准确性和收敛速度.实验结果表明:灰狼算法优化的BP神经网络具有较好的性能和泛化能力.其次,还用股票数据进行了实证分析,该模型在股票价格预测方面具有较高的准确性和稳定性,可为投资者提供有效的决策参考.最后,总结了本研究的贡献和未来的研究方向.
Abstract
The method of improving the BP neural network by the grey wolf optimizer is discussed,in order to im-prove the training effect and performance of the BP neural network.Firstly,the basic principle of the BP neural net-work and the basic concept of the grey Wolf optimizer are introduced.Then,the grey wolf optimizer is applied to the process of optimizing the weight and bias value of the BP neural network,and the error function is reduced by adjusting these parameters,so as to improve the accuracy and convergence speed of the network.Experimental re-sults show that the BP neural network optimized by the grey wolf optimizer has good performance and generaliza-tion ability.Next,empirical analysis is carried out with stock data,showing that the model has high accuracy and stability in stock price prediction and can provide effective reference for investors to make decisions.Finally,the contribution of this study and the future research direction are summarized.
关键词
灰狼算法/BP神经网络/参数优化/股价预测Key words
Gray wolf optimizer/BP neural network/Parameter optimization/Stock price prediction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据