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Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator

Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator

Ming Zhang
Copyright: © 2016 |Pages: 34
ISBN13: 9781522500636|ISBN10: 1522500634|EISBN13: 9781522500643
DOI: 10.4018/978-1-5225-0063-6.ch001
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MLA

Zhang, Ming . "Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator." Applied Artificial Higher Order Neural Networks for Control and Recognition, edited by Ming Zhang, IGI Global, 2016, pp. 1-34. https://doi.org/10.4018/978-1-5225-0063-6.ch001

APA

Zhang, M. (2016). Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator. In M. Zhang (Ed.), Applied Artificial Higher Order Neural Networks for Control and Recognition (pp. 1-34). IGI Global. https://doi.org/10.4018/978-1-5225-0063-6.ch001

Chicago

Zhang, Ming . "Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator." In Applied Artificial Higher Order Neural Networks for Control and Recognition, edited by Ming Zhang, 1-34. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0063-6.ch001

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Abstract

This chapter develops a new nonlinear model, Ultra high frequency Polynomial and Trigonometric Higher Order Neural Networks (UPT-HONN), for control signal generator. UPT-HONN includes UPS-HONN (Ultra high frequency Polynomial and Sine function Higher Order Neural Networks) and UPC-HONN (Ultra high frequency Polynomial and Cosine function Higher Order Neural Networks). UPS-HONN and UPC-HONN model learning algorithms are developed in this chapter. UPS-HONN and UPC-HONN models are used to build nonlinear control signal generator. Test results show that UPS-HONN and UPC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UPS-HONN and UPC-HONN models can generate control signals with error approaching 0.0000%.

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