Liu Guo-rong

Collaborative Filtering Based Recommendation System for Product Bundling

Due to the convenience of Internet, people can search for whatever information they need and buy whatever they want on the Web. In the age of e-commerce, it is important for firms to develop Web-based marketing strategy such as product bundling to increase revenue. Recommendation system (RS) is a platform that can be used to reduce the searching cost of consumers, increase the effectiveness of firms promotion strategies and enhance consumers loyalty. This paper proposes a Web-based collaborative filtering mechanism for firms product bundling strategy. First, clustering using adaptive resonance theory is applied to generate different kinds of customer group, in which members have same attributes or habits. Second, data mining using association rule techniques is applied to find the relations between two products in given support and confidence values. And last, the RS value is calculated to generate personal product bundling list and top-N recommendations. Experiment results also show that it is a feasible and effective design scheme

A Review on Localization and Mapping Algorithm Based on Extended Kalman Filtering

Simultaneous localization and mapping (SLAM) algorithm for mobile robots is a key problem in the field of robotics, the determination of the SLAM problems also has gained significant research momentum in recent times. And extended Kalman filter (EKF) algorithm is the most widely used algorithm in the study of SLAM problem. In this paper, the latest progress of SLAM algorithms based on EKF is surveyed, and the key techniques adopted. Firstly, the fundamental philosophy and current situation of the EKF algorithm in the study is summarized, pointed out its drawbacks and improvements of EKF. Secondly, constructed the general model of SLAM problem, expatiated the fundamental method and current situation on SLAM algorithms based on EKF. Finally, from the trend of recent study and the existence of difficult problems, we present future research trend of EKF approaches in SLAM problem.

Synchronization and Parameters Identification of Uncertain Fractional-Order Chaotic System Using Integer-Order System

The synchronization and parameter identification problem of the uncertain fractional-order chaotic system is investigated. Based on the Lyapunov stability theory and adaptive control theory, a method for Synchronization of uncertain fractional-order chaotic system using integer-order system is proposed, a novel adaptive controller and parameter updating rule are designed. Synchronization fractional-order chaotic Chen system using integral-order chaotic Chen system is realized, by which the uncertain parameters of Chen system are identified. Simulation results also verify the effectiveness of the proposed algorithm.