New Neural Network Design for Approximate Dynamic Programming and Optimal Multiuser Detection

In this thesis we demonstrate that a new neural network design can be used to solve a class of difficult function approximation problems which are crucial to the field of approximate dynamic programming (ADP). Although conventional neural networks have been proven to approximate smooth functions very well, the use of ADP for problems of intelligent control or planning requires the approximation of functions which are not so smooth. As an example, this thesis studies the problem of approximating the $J$ function of dynamic programming applied to the task of navigating mazes, in general, without the need to learn each individual maze. Conventional neural networks, like multi-layer perceptrons (MLPs), cannot learn this task. But a new type of neural network, simultaneous recurrent networks (SRNs), can accomplish the required learning as demonstrated by successful initial tests. In this thesis we also investigate the ability of recurrent neural networks to approximate MLPs and vice versa. Moreover, we present a comparison between using SRNs and MLPs to implement the optimal CDMA multiuser detector (OMD). This example is intended to demonstrate that SRNs can provide fast suboptimal solutions to hard combinatorial optimization problems, and achieve better bit- error-rate (BER) performance than MLPs

RESEARCH ON VIDEO-BASED HUMAN BODY MOTION TRACKING

The video-based motion capture system uses cheap equipments, like digital cameras and personal computers, to track a human motion without any sensors or markers attached to the body. This topic has a wide application in areas such as smart surveillance, human computer interaction and athletic performance analysis etc., and it becomes a hot topic of computer vision in recent years. Because of the complexity of the problem and lack of comprehension of human vision system essence, visual tracking is still hard in computer vision

Chinese Semantic Class Learning from Web Based on Concept-Level Characteristics

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Research on Mechanism of Knowledge diffuseness in Technology Innovation

Technology innovation means the complete activity that begins with new idea from technology, passes through research and development or technologies combination, goes up to obtaining practical application and gives birth to the economic or social benefits. Its essence is the course of knowledge diffuseness. By the analysis on the phases in the course of technology innovation, we can design the mechanism of knowledge diffuseness in technology innovation from the following four parts: socialization, externalization, combination and internalization. It is helpful for people to understand in profound the course of technology innovation from the point of view of knowledge process.The original publication is available at JAIST Press http://www.jaist.ac.jp/library/jaist-press/index.htmlKICSS 2007 : The Second International Conference on Knowledge, Information and Creativity Support Systems : PROCEEDINGS OF THE CONFERENCE, November 5-7, 2007, [Ishikawa High-Tech Conference Center, Nomi, Ishikawa, JAPAN