Ming Zhang

Ming Zhang received a M.S. degree in information processing and a Ph.D. degree in the research area of computer vision from the East China Normal University, Shanghai, China, in 1982 and 1989, respectively. He held Postdoctoral Fellowships in artificial neural networks with the Shanghai Institute of Technical Physics, Chinese Academy of the Sciences in 1989 and the National Oceanic and Atmospheric Administration, USA National Research Council in 1991. He was a face recognition airport security system project manager and was a Ph.D. co-supervisor at the University of Wollongong, Australia in 1992. Since 1994, he has been a lecturer at the Monash University, Australia. From 1995 to 1999, he was a lecturer and a senior lecturer and a Ph.D. supervisor at the University of Western Sydney, Australia. He also held a Senior Research Associate Fellowship in artificial neural networks with the National Oceanic and Atmospheric Administration, USA National Research Council in 1999. Since 2000, he has been an associate professor at Christopher Newport University, VA, USA. He is currently a full Professor and a graduate student supervisor in computer science at the Christopher Newport University, VA, USA.

Publications

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks
Ming Zhang. © 2021. 540 pages.
Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks...
Artificial Higher Order Neural Network Models
Ming Zhang. © 2017. 71 pages.
This chapter introduces the background of HONN model developing history and overview 24 applied artificial higher order neural network models. This chapter provides 24 HONN...
Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator
Ming Zhang. © 2017. 34 pages.
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...
Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition
Ming Zhang. © 2017. 34 pages.
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...
Artificial Sine and Cosine Trigonometric Higher Order Neural Networks for Financial Data Prediction
Ming Zhang. © 2017. 29 pages.
This chapter develops two new nonlinear artificial higher order neural network models. They are Sine and Sine Higher Order Neural Networks (SIN-HONN) and Cosine and Cosine Higher...
Cosine and Sigmoid Higher Order Neural Networks for Data Simulations
Ming Zhang. © 2017. 16 pages.
New open box and nonlinear model of Cosine and Sigmoid Higher Order Neural Network (CS-HONN) is presented in this paper. A new learning algorithm for CS-HONN is also developed...
Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification
Ming Zhang. © 2017. 41 pages.
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...
Applied Artificial Higher Order Neural Networks for Control and Recognition
Ming Zhang. © 2016. 511 pages.
In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear...
Ultra High Frequency Polynomial and Trigonometric Higher Order Neural Networks for Control Signal Generator
Ming Zhang. © 2016. 34 pages.
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...
Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition
Ming Zhang. © 2016. 33 pages.
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...
Ultra High Frequency SINC and Trigonometric Higher Order Neural Networks for Data Classification
Ming Zhang. © 2016. 41 pages.
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...
Artificial Sine and Cosine Trigonometric Higher Order Neural Networks for Financial Data Prediction
Ming Zhang. © 2016. 29 pages.
This chapter develops two new nonlinear artificial higher order neural network models. They are Sine and Sine Higher Order Neural Networks (SIN-HONN) and Cosine and Cosine Higher...
Cosine and Sigmoid Higher Order Neural Networks for Data Simulations
Ming Zhang. © 2016. 16 pages.
New open box and nonlinear model of Cosine and Sigmoid Higher Order Neural Network (CS-HONN) is presented in this paper. A new learning algorithm for CS-HONN is also developed...
Artificial Higher Order Neural Network Models
Ming Zhang. © 2016. 69 pages.
This chapter introduces the background of HONN model developing history and overview 24 applied artificial higher order neural network models. This chapter provides 24 HONN...
Artificial Higher Order Neural Networks for Modeling and Simulation
Ming Zhang. © 2013. 454 pages.
With artificial neural network research being one of the new directions for new generation computers, current research suggests that open-box artificial higher order neural...
Artificial Multi-Polynomial Higher Order Neural Network Models
Ming Zhang. © 2013. 29 pages.
This chapter introduces Multi-Polynomial Higher Order Neural Network (MPHONN) models with higher accuracy. Using Sun workstation, C++, and Motif, a MPHONN Simulator has been...
Artificial Polynomial and Trigonometric Higher Order Neural Network Group Models
Ming Zhang. © 2013. 25 pages.
Real world financial data is often discontinuous and non-smooth. Accuracy will be a problem, if we attempt to use neural networks to simulate such functions. Neural network group...
Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications
Ming Zhang. © 2010. 660 pages.
Artificial neural network research is one of the promising new directions for the next generation of computers and open box artificial Higher Order Neural Networks (HONNs) play...
Higher Order Neural Network Group-based Adaptive Tolerance Trees
Ming Zhang. © 2010. 36 pages.
Recent artificial higher order neural network research has focused on simple models, but such models have not been very successful in describing complex systems (such as face...
Rainfall Estimation Using Neuron-Adaptive Higher Order Neural Networks
Ming Zhang. © 2010. 28 pages.
Real world data is often nonlinear, discontinuous and may comprise high frequency, multi-polynomial components. Not surprisingly, it is hard to find the best models for modeling...
Artificial Higher Order Neural Networks for Economics and Business
Ming Zhang. © 2009. 542 pages.
Artificial Higher Order Neural Networks (HONNs) significantly change the research methodology that is used in economics and business areas for nonlinear data simulation and...
Artificial Higher Order Neural Network Nonlinear Models: SAS NLIN or HONNs?
Ming Zhang. © 2009. 47 pages.
This chapter delivers general format of Higher Order Neural Networks (HONNs) for nonlinear data analysis and six different HONN models. This chapter mathematically proves that...
Ultra High Frequency Trigonometric Higher Order Neural Networks for Time Series Data Analysis
Ming Zhang. © 2009. 31 pages.
This chapter develops a new nonlinear model, Ultra high frequency Trigonometric Higher Order Neural Networks (UTHONN), for time series data analysis. Results show that UTHONN...
Application of Higher-Order Neural Networks to Financial Time-Series Prediction
John Fulcher, Ming Zhang, Shuxiang Xu. © 2006. 29 pages.
Financial time-series data is characterized by nonlinearities, discontinuities, and high-frequency multipolynomial components. Not surprisingly, conventional artificial neural...