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

This chapter proposes a novel anti-swing control strategy for an overhead crane. The controller includes both position regulation and anti-swing control. Since the crane model is not exactly known, fuzzy rules are used to compensate friction, gravity as well as the coupling between position and anti-swing control. A highgain observer is introduced to estimate the joint velocities to realize PD control. Using a Lyapunov method and an input-to-state stability technique, the controller is proven to be robustly stable with bounded uncertainties, if the membership functions are changed by certain learning rules and the observer is fast enough. Real-time experiments are presented comparing this new stable anti-swing PD control strategy with regular crane controllers.

Stable Anti-Swing Control for an Overhead Crane with Velocity Estimation and Fuzzy Compensation

Many cascade processes, such as wastewater treatment plant, include complex nonlinear sub-systems and many variables. The normal input-output relation only represent the first block and the last block of the cascade process. In order to model the whole process. We use hierarchical dynamic neural networks to identify the cascade process. The internal variables of the cascade process are estimated. Two stable learning algorithms and theoretical analysis are given. Real operational data of a wastewater treatment plant are applied to illustrate this new neural modeling approach.

Hierarchical dynamic neural networks for cascade system modeling with application to wastewater treatment

Gasoline blending is an important unit operation in gasoline industry. A good model for the blending system is beneficial for supervision operation, prediction of the gasoline qualities and performing model-based optimal control. Gasoline blending process involves two types of proprieties: static blending and dynamic in the blending tanks. The blending process cannot be modeled exactly, because it does not follow ideal mixing rules in practice. In this paper we propose a hybrid neural network, which uses static and dynamic neural networks to approximate the blending properties. Numerical simulations are provided to illustrate the neuro modeling approach.

Hybrid neural networks for gasoline blending system modeling

A common problem in robot navigation is behaviour rule selection when more than one action of the same type is available. Here action selection or decision-making procedure incorporating α-level fuzzy logic is discussed and used for selecting an appropriate action during mobile robot navigation. In the present approach, the operational strategies of the human expert driver are transferred via fuzzy logic to the robot navigation in the form of optimising behaviour rules without decreasing the input space and resolved the conflicts similar to human thinking. This paper presents the mathematical aspects of resolving conflicts when more than one context rule of the same kind is in action. Simulation results are presented based on the real life situation. The techniques presented in this paper are validated by conducting real world experiments using Khepera miniature Robot.

Stable antiswing PD control for overhead crane systems with velocity estimation and uncertainties compensation

Normal fuzzy CMAC neural network performs well because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. In this paper, we use recurrent technique to overcome these problems and propose a new CMAC neural network, named recurrent fuzzy CMAC (RFCMAC). Since the structure of RFCMAC is more complex, normal training methods are difficult to be applied. A new simple algorithm with a time-varying learning rate is proposed to assure the learning algorithm is stable.

Xiaoou Li

Papers

Stable Anti-Swing Control for an Overhead Crane with Velocity Estimation and Fuzzy Compensation

Hierarchical dynamic neural networks for cascade system modeling with application to wastewater treatment

Hybrid neural networks for gasoline blending system modeling

Stable antiswing PD control for overhead crane systems with velocity estimation and uncertainties compensation

Recurrent Fuzzy CMAC for Nonlinear System Modeling