Financial Data Prediction by Artificial Sine and Cosine Trigonometric Higher Order Neural Networks

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引用次数: 1

Abstract

This chapter develops two new nonlinear artificial higher order neural network models. They are sine and sine higher order neural networks (SIN-HONN) and cosine and cosine higher order neural networks (COS-HONN). Financial data prediction using SIN-HONN and COS-HONN models are tested. Results show that SIN-HONN and COS-HONN models are good models for some sine feature only or cosine feature only financial data simulation and prediction compared with polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models.
人工正弦和余弦三角高阶神经网络的金融数据预测
本章建立了两种新的非线性人工高阶神经网络模型。它们是正弦和正弦高阶神经网络(SIN-HONN)和余弦和余弦高阶神经网络(COS-HONN)。分别对SIN-HONN和COS-HONN模型进行了金融数据预测试验。结果表明,与多项式型高阶神经网络(PHONN)和三角型高阶神经网络(THONN)模型相比,SIN-HONN和COS-HONN模型对某些仅含正弦特征或仅含余弦特征的金融数据进行仿真和预测是较好的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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