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

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

DOI: 10.4018/978-1-7998-3563-9.ch011
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Abstract

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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Introduction

The contributions of this chapter will be:

  • Introduce the background of HONNs with the pattern recognition of HONNs.

  • Develop a new UGT-HONN model for ultra-high frequency data pattern recognition.

  • Provide the UGT-HONN learning algorithm and weight update formulae.

  • Applications of UGT-HONN model for data pattern recognition.

This chapter is organized as follows: Section “BACKGROUND” gives the background knowledge of HONNs and pattern recognition applications using HONNs. Section “UGT-HONN MODELS” introduces UGT-HONN structure and different modes of the UGT-HONN model. Section LEARNING ALGORITHM OF UGT-HONN MODELS provides the UGT-HONN model update formula, learning algorithms, and convergence theories of HONN. Section “UGT-HONN TESTING” describes UGT-HONN computer software system and testing results for data pattern recognition.

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Background

Artificial Neural Network (ANN) techniques had been widely used in the pattern recognition area. Sankar and Mammone (1991) study speaker independent vowel recognition using neural tree networks. Sethi and Jan (1991) analyze decision tree performance enhancement using an artificial neural networks implementation. Yao, Freeman, Burke, and Yang (1991) experiment pattern recognition by a distributed neural network.

Key Terms in this Chapter

UGT-HONN: Artificial ultra-high frequency sigmoid and trigonometric higher order neural network.

UGC-HONN: Artificial ultra-high frequency sigmoid and cosine higher order neural network.

PHONN: Artificial polynomial higher order neural network.

UGS-HONN: Artificial ultra-high frequency sigmoid and sine higher order neural network.

HONN: Artificial higher order neural network.

THONN: Artificial trigonometric higher order neural network.

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