Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications: Trends for Emerging Applications

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Zhang, Ming
IGI Global, Feb 28, 2010 - Computers - 660 pages
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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 an important role in this future.

Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks. Since HONNs are open box models, they can be easily used in information science, information technology, management, economics, and business. This book details the techniques, theory and applications essential to engaging and capitalizing on this developing technology.

 

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Contents

Higher Order Neural Network GroupBased Adaptive Tolerance Trees
1
Higher Order Neural Networks for Symbolic SubSymbolicand Chaotic Computations
37
Evolutionary Algorithm Training of Higher Order Neural Networks
57
Adaptive Higher Order Neural Network Models for Data Mining
86
Robust Adaptive Control Using Higher Order Neural Networks and Projection
99
On the Equivalence Between Ordinary Neural Networks and Higher Order Neural Networks
138
Rainfall Estimation Using NeuronAdaptive Higher Order Neural Networks
159
Analysis of Quantization Effects on Higher Order Function and Multilayer Feedforward Neural Networks
187
Artificial Higher Order Neural Network Training on Limited Precision Processors
312
Recurrent Higher Order Neural Observers for Anaerobic Processes
333
Electric Machines Excitation Control via Higher Order Neural Networks
366
Fundamental Theory and Applications
397
Identification of Nonlinear Systems Using a New NeuroFuzzy Dynamical System Definition Based on High Order Neural Network Function Approxi...
423
NeuroFuzzy Control Schemes Based on High Order Neural Network Function Approximators
450
A Dynamically Tuned Higher Order Like Neural Network Approach
484
Artificial Tactile Sensing and Robotic Surgery Using Higher Order Neural Networks
514

Improving Sparsity in Kernelized Nonlinear Feature Extraction Algorithms by Polynomial Kernel Higher Order Neural Networks
223
Analysis and Improvement of Function Approximation Capabilities of PiSigma Higher Order Neural Networks
239
Dynamic Ridge Polynomial Higher Order Neural Network
255
Fifty Years of Electronic Hardware Implementations of First and Higher Order Neural Networks
269
Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs
286
A Theoretical and Empirical Study of Functional Link Neural Networks FLNNs for Classification
545
Compilation of References
574
About the Contributors
614
Index
626
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About the author (2010)

Ming Zhang was born in Shanghai, China. He received the MS degree in information processing and PhD degree in the research area of computer vision from East China Normal University, Shanghai, China, in 1982 and 1989, respectively. He held Postdoctoral Fellowships in artificial neural networks with the Chinese Academy of the Sciences in 1989 and the USA National Research Council in 1991. He was a face recognition airport security system project manager and PhD co-supervisor at the University of Wollongong, Australia in 1992. Since 1994, he was a lecturer at the Monash University, Australia, with a research area of artificial neural network financial information system. From 1995 to 1999, he was a senior lecturer and PhD supervisor at the University of Western Sydney, Australia, with the research interest of artificial neural networks. He also held Senior Research Associate Fellowship in artificial neural networks with the USA National Research Council in 1999. He is currently a Full Professor and graduate student supervisor in computer science at the Christopher Newport University, VA, USA. With more than 100 papers published, his current research includes artificial neural network models for face recognition, weather forecasting, financial data simulation, and management. [Editor]

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