基于超高频s型和三角高阶神经网络的数据模式识别

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks Pub Date : 1900-01-01 DOI:10.4018/978-1-7998-3563-9.ch011

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摘要

本章发展了一种新的用于数据模式识别的非线性模型，超高频s型和三角高阶神经网络(UGT-HONN)。UGT-HONN包括超高频s型和正弦函数高阶神经网络(UGS-HONN)和超高频s型和余弦函数高阶神经网络(UGC-HONN)。采用UGS-HONN和UGC-HONN模型对数据模式进行识别。结果表明，UGS-HONN和UGC-HONN模型的数据模式识别误差接近10-6，优于其他多项式高阶神经网络(PHONN)和三角高阶神经网络(THONN)模型。

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Data Pattern Recognition Based on Ultra-High Frequency Sigmoid and Trigonometric Higher Order Neural Networks

This chapter develops a new nonlinear model, ultra high frequency sigmoid and trigonometric higher order neural networks (UGT-HONN), for data pattern recognition. UGT-HONN includes ultra high frequency sigmoid and sine function higher order neural networks (UGS-HONN) and ultra high frequency sigmoid and cosine functions higher order neural networks (UGC-HONN). UGS-HONN and UGC-HONN models are used to recognition data patterns. Results show that UGS-HONN and UGC-HONN models are better than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models, since UGS-HONN and UGC-HONN models can recognize data pattern with error approaching 10-6.

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Emerging Capabilities and Applications of Artificial Higher Order Neural Networks

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