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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.
- Sales Rank: #8954566 in Books
- Brand: Brand: Information Science Reference
- Published on: 2010-02-01
- Original language: English
- Number of items: 1
- Dimensions: 11.02" h x 1.44" w x 8.50" l, 4.65 pounds
- Binding: Hardcover
- 378 pages
- Used Book in Good Condition
About the Author
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.
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