报告内容：

　　An optimal weight learning machine for a single-hidden layer feedforward network (SLFN) with application to handwritten digit image recognition is developed. It is seen that both the input weights and the output weights of the SLFN are globally optimized with the batch learning type of least squares. All feature vectors of the classifier can then be placed at the prescribed positions in the feature space in the sense that the separability of all nonlinearly separable patterns can be maximized, and a high degree of recognition accuracy can be achieved with a small number of hidden nodes in the SLFN. An experiment for the recognition of the handwritten digit image from both the MNIST database is performed to show the excellent performance and effectiveness of the proposed methodology.

 

　　报告人简介：Zhihong Man，Professor,

　　Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Australia

　　Zhihong Man received his B.E. degree from Shanghai Jiaotong University, China, in 1982, the M.Sc. degree from Chinese Academy of Sciences in 1987, and the Ph.D. degree from the University of Melbourne, Australia, in 1994, respectively.

　　From 1994 to 1996, he was the Lecturer in the School of Engineering, Edith Cowan University, Australia. From 1996 to 2001, he was the Lecturer and then the Senior Lecturer in the School of Engineering, The University of Tasmania, Australia. From 2002 to 2007, he was the Associate Professor of Computer Engineering at Nanyang Technological University, Singapore. From 2007 to 2008, he was the Professor and Head of Electrical and Computer Systems Engineering, Monash University Sunway Campus, Malaysia. Since 2009, he has been the Professor and Head of Robotics and Mechatronics in the Faculty of Engineering and Industrial Sciences, Swinburne University of Technology, Australia.

　　His research is in the areas of robotics, nonlinear control systems and neural network-based signal processing, vehicle dynamics and control. He published a major research reference text book on Robotics (by Prentice Hall in 2005) that has been widely used by more than 100 universities in the world as both the senior robotics text and research reference.

　　Professor Man with Professor Yu (RMIT) worked together to develop the theory of terminal sliding mode control in 1990s‘, which has been widely applied by the researchers in the world for the control of linear systems, nonlinear systems and complex systems with uncertain dynamics since then. He currently works closely with the Defence Science and Technology Organization, Australia, designing detection, prediction and prognosis systems for the gearboxes in helicopter systems. His combined publications have been cited more than 2000 times with H-index 19 and at least 8 publications achieving citation rates in excess of 100 according to Publish or Perish Statistics.

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