Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition

Cosine Functions

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 to recognize data pattern with error approaching 0.0000%.

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks - Advances in Computational Intelligence and Robotics ◽

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

10.4018/978-1-7998-3563-9.ch011 ◽

2021 ◽

pp. 455-497

Keyword(s):

Neural Network ◽

Neural Networks ◽

Pattern Recognition ◽

Nonlinear Model ◽

High Frequency ◽

Higher Order ◽

Sine Function ◽

Ultra High Frequency ◽

Cosine Functions

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.

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks - Advances in Computational Intelligence and Robotics ◽

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

10.4018/978-1-7998-3563-9.ch007 ◽

2021 ◽

pp. 303-345

Keyword(s):

Neural Network ◽

Neural Networks ◽

Nonlinear Model ◽

High Frequency ◽

Data Classification ◽

Higher Order ◽

Ultra High Frequency ◽

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.

Advances in Computational Intelligence and Robotics - Applied Artificial Higher Order Neural Networks for Control and Recognition ◽

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

10.4018/978-1-5225-0063-6.ch005 ◽

2016 ◽

pp. 113-153 ◽

Cited By ~ 1

Author(s):

Ming Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Nonlinear Model ◽

High Frequency ◽

Data Classification ◽

Higher Order ◽

Ultra High Frequency ◽

Better Than

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%.

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

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch031 ◽

2016 ◽

pp. 789-829

Author(s):

Ming Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Nonlinear Model ◽

High Frequency ◽

Data Classification ◽

Higher Order ◽

Ultra High Frequency ◽

Better Than

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%.

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks - Advances in Computational Intelligence and Robotics ◽

Time Series Data Analysis by Ultra-High Frequency Trigonometric Higher Order Neural Networks

10.4018/978-1-7998-3563-9.ch005 ◽

2021 ◽

pp. 218-261

Keyword(s):

Neural Network ◽

Neural Networks ◽

Exchange Rates ◽

High Frequency ◽

Time Series Data ◽

Higher Order ◽

Series Data ◽

Ultra High Frequency ◽

Time Series Data Analysis

This chapter develops a new nonlinear model, ultra high frequency trigonometric higher order neural networks (UTHONN) for time series data analysis. UTHONN includes three models: UCSHONN (ultra high frequency sine and cosine higher order neural networks) models, UCCHONN (ultra high frequency cosine and cosine higher order neural networks) models, and USSHONN (ultra high frequency sine and sine higher order neural networks) models. Results show that UTHONN models are 3 to 12% better than equilibrium real exchange rates (ERER) model, and 4–9% better than other polynomial higher order neural network (PHONN) and trigonometric higher order neural network (THONN) models. This study also uses UTHONN models to simulate foreign exchange rates and consumer price index with error approaching 10-6.

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks - Advances in Computational Intelligence and Robotics ◽

Models of Artificial Higher Order Neural Networks

10.4018/978-1-7998-3563-9.ch001 ◽

2021 ◽

pp. 1-96

Keyword(s):

Neural Network ◽

Neural Networks ◽

High Frequency ◽

Learning Algorithm ◽

Network Models ◽

Higher Order ◽

Neural Network Models ◽

Ultra High Frequency ◽

This chapter introduces the background of the higher order neural network (HONN) model developing history and overviews 24 applied artificial higher order neural network models. This chapter provides 24 HONN models and uses a single uniform HONN architecture for all 24 HONN models. This chapter also uses a uniform learning algorithm for all 24 HONN models and uses uniform weight update formulae for all 24 HONN models. In this chapter, polynomial HONN, Trigonometric HONN, Sigmoid HONN, SINC HONN, and Ultra High Frequency HONN structure and models are overviewed too.

Advances in Computational Intelligence and Robotics - Applied Artificial Higher Order Neural Networks for Control and Recognition ◽

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

10.4018/978-1-5225-0063-6.ch001 ◽

2016 ◽

pp. 1-34 ◽

Cited By ~ 1

Author(s):

Ming Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

High Frequency ◽

Higher Order ◽

Signal Generator ◽

Control Signal ◽

Test Results ◽

Model Learning ◽

Ultra High Frequency ◽

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%.

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

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch026 ◽

2016 ◽

pp. 648-681

Author(s):

Ming Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

High Frequency ◽

Higher Order ◽

Signal Generator ◽

Control Signal ◽

Test Results ◽

Model Learning ◽

Ultra High Frequency ◽

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%.

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks - Advances in Computational Intelligence and Robotics ◽

Control Signal Generator Based on Ultra-High Frequency Polynomial and Trigonometric Higher Order Neural Networks

10.4018/978-1-7998-3563-9.ch010 ◽

2021 ◽

pp. 416-454

Keyword(s):

Neural Network ◽

Neural Networks ◽

High Frequency ◽

Higher Order ◽

Signal Generator ◽

Control Signal ◽

Test Results ◽

Model Learning ◽

Ultra High Frequency ◽

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 10-6.