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

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

ISBN13: 9781522500636|ISBN10: 1522500634|EISBN13: 9781522500643
DOI: 10.4018/978-1-5225-0063-6.ch005
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MLA

Zhang, Ming . "Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification." Applied Artificial Higher Order Neural Networks for Control and Recognition, edited by Ming Zhang, IGI Global, 2016, pp. 113-153. https://doi.org/10.4018/978-1-5225-0063-6.ch005

APA

Zhang, M. (2016). Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification. In M. Zhang (Ed.), Applied Artificial Higher Order Neural Networks for Control and Recognition (pp. 113-153). IGI Global. https://doi.org/10.4018/978-1-5225-0063-6.ch005

Chicago

Zhang, Ming . "Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification." In Applied Artificial Higher Order Neural Networks for Control and Recognition, edited by Ming Zhang, 113-153. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0063-6.ch005

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Abstract

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 better 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 the data with error approaching 0.0000%.

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