Dynamic Vision via Deterministic Learning

摘要：

The recovery of three dimensional structure and motion from time vary images with the aid of CCD camera(s) is usually performed using a nonlinear dynamic system, often referred to as a perspective dynamic system, where the major task is formulated as the problem of state estimation and parameter estimation. A Luenberger-type observer can be used to measure the constant motion parameter system states when only the output of the perspective system is measurable. In this paper, based on the recent results on deterministic learning theory, when the system states are periodic or recurrent, RBF neural networks can satisfy the partial PE condition along the states, the system dynamics will be learned by RBF neural networks and saved in a way of constant RBF neural networks, and the learning error converges exponentially to a small neighborhood of zero. Take the constant RBF neural networks achieved as training pattern to form the bank of system dynamical patterns, and before that similarity definition is given. When meeting new system dynamics which are considered as test patterns, it can be used to achieve rapid recognition of system dynamical patterns between the test and training dynamical patterns.

作者: Huiyan Yang Wei Zeng Cong Wang

作者单位: College of Automation, South China University of Technology Guangzhou, 510640, China College of Automation, South China University of Technology Guangzhou, 510640, China College of Phys College of Automation South China University of Technology Guangzhou, 510640, China

会议类型: 国际会议

会议名称: The 8th World Congress on Intelligent Control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

会议地点: 济南

会议语种:英文

页码: 542-547

在线出版日期: 2010-07-07（万方平台首次上网日期，不代表论文的发表时间）