다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining - Concepts and Techniques.

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

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A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

International Joint Conference on Neural Networks

A comprehensive survey on support vector machine classification: Applications, challenges and trends

The control problem of a multicomponent distillation column is discussed in this paper. A new dynamic mathematical model of nonideal distillation process is derived. Since it contains an implicit recursive procedure, the traditional identification methods cannot be applied, a differential neural network is used to identify this system. Based on the neural identifier, a local optimal neuro controller is proposed. Numerical simulations, concerning a distillation column with 5 components and 15 trays, illustrate the effectiveness of the suggested approach.

Modeling and neuro control for multicomponent nonideal distillation column

The human brain has self-organization capability after paralysis caused by stork. This needs neuro-rehabilitation for stroke-patients. Recently, robotic technology is successfully applied in the rehabilitation. Most of robot rehabilitation devices are in 2-dimension, and do not have feedback from the environment. The rehabilitation effectiveness is not so good. In this paper, we design a 3-demension rehabilitation system, which has force feedback. The patients have one more training dimension and one more sense (haptic feeling). The neuro recovery time is less than the other robot rehabilitation methods.

A 3-D hand rehabilitation system using haptic device

Combination of neural networks and sliding mode control (SMC) can reduce chattering, because the upper bound of uncertainties becomes smaller when neural networks are used to model unknwn nolinear systems. The tracking error of normal neural sliding mode control is asymptotically stable, while neural control and SMC are applied at same time. In this paper, neural control and SMC are connected serially: first a deadzone neural control assures that the tracking error is bounded, then super-twisting secondorder slidingmode is used to guarantee finite time convergence of the contoller.

Neural sliding mode control with finite time convergence

Object Oriented Colored Petri Net (OOCPN) was proposed for DEDS (discrete event dynamic systems) modeling and simulation, especially it has been studied deeply in FMS (flexible manufacturing systems) (1995). However, for semiconductor manufacturing systems, it can not be applied directly since this kind of systems have some hybrid conception which OOCPN cannot describe. In this paper we extend OOCPN to hybrid conception by enhancing it with time delay and firing speed, and use this hybrid-like OOCPN for semiconductor manufacturing systems. A specific system (1998) is used to illustrate and demonstrate the proposed model.

Modeling an electronic component manufacturing system using Object Oriented Colored Petri Nets

Autonomous navigation in unknown environment is a big challenge. In this paper, we combine the SLAM (simultaneous localization and mapping) with the path planning method. We first modify the classical SLAM with sliding mode technique, such that it is robust in the unknown environment. Then we analyze the algorithm using the “known space” and “free space” conditions, and propose the polar histogram path planning based on these conditions. We use Monte Carlo method to evaluate the performance of our algorithms. Simulation results show that our autonomous navigation algorithms are better than the others in unknown environment.

Xiaoou Li

Papers

Modeling and neuro control for multicomponent nonideal distillation column

A 3-D hand rehabilitation system using haptic device

Neural sliding mode control with finite time convergence

Modeling an electronic component manufacturing system using Object Oriented Colored Petri Nets

Autonomous navigation in unknown environments using robust SLAM