基于超高频SINC和三角高阶神经网络的数据分类

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks Pub Date : 1900-01-01 DOI:10.4018/978-1-7998-3563-9.ch007

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摘要

本章发展了一种新的非线性模型，超高频正弦三角高阶神经网络(UNT-HONN)，用于数据分类。UNT-HONN包括超高频正弦和正弦高阶神经网络(UNS-HONN)和超高频正弦和余弦高阶神经网络(UNC-HONN)。使用uncs - honn和uncs - honn模型进行了数据分类测试。结果表明，UNS-HONN和UNC-HONN模型比其他多项式高阶神经网络(PHONN)和三角高阶神经网络(THONN)模型更准确，因为UNS-HONN和UNC-HONN模型的分类误差接近10-6。

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Data Classification Using Ultra-High Frequency SINC and Trigonometric Higher Order Neural Networks

This chapter develops a new nonlinear model, ultra high frequency sinc and trigonometric higher order neural networks (UNT-HONN), for data classification. UNT-HONN includes ultra high frequency sinc and sine higher order neural networks (UNS-HONN) and ultra high frequency sinc and cosine higher order neural networks (UNC-HONN). Data classification using UNS-HONN and UNC-HONN models are tested. Results show that UNS-HONN and UNC-HONN models are more accurate than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models, since UNS-HONN and UNC-HONN models can classify data with error approaching 10-6.

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Emerging Capabilities and Applications of Artificial Higher Order Neural Networks

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