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

In general, fuzzy neural networks cannot match nonlinear systems exactly. Unmodeled dynamic leads parameters drift and even instability problem. According to system identification theory, robust modification terms must be included in order to guarantee Lyapunov stability. This paper suggests new learning laws for Mamdani and Takagi-Sugeno-Kang type fuzzy neural networks based on input-to-state stability approach. The new learning schemes employ a time-varying learning rate that is determined from input-output data and model structure. Stable learning algorithms for the premise and the consequence parts of fuzzy rules are proposed. The calculation of the learning rate does not need any prior information such as estimation of the modeling error bounds. This offer an advantage compared to other techniques using robust modification.

Fuzzy identification using fuzzy neural networks with stable learning algorithms

Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain senses. We conclude that the gradient descent algorithm for weight adjustment is stable in an L/sub /spl infin// sense and robust to any bounded uncertainties.

Some new results on system identification with dynamic neural networks

Support vector machine classification for large data sets via minimum enclosing ball clustering

Since knowledge in an expert system is vague and modified frequently, expert systems are fuzzy and dynamic. It is very important to design a dynamic knowledge inference framework which is adjustable according to knowledge variation as human cognition and thinking. A generalized fuzzy Petri net model, called adaptive fuzzy Petri net (AFPN), is proposed with this object in mind. AFPN not only has the descriptive advantages of the fuzzy Petri net, it also has learning ability like a neural network. Just as other fuzzy Petri net (FPN) models, AFPN can be used for knowledge representation and reasoning, but AFPN has one important advantage: it is suitable for dynamic knowledge, i.e., the weights of AFPN are adjustable. Based on the AFPN transition firing rule, a modified backpropagation learning algorithm is developed to assure the convergence of the weights.

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Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework

The unique exoskeleton system (EXO-UL7) in UCSC is controlled in two levels. The lower-level uses standard PID control. Three force sensors in the upper-level send desired trajectories to the lower-level. The impedance/admittance control can is limit both internal and contact forces. It is impossible to design a model-based impedance/admittance control when the model of the exoskeleton is unavailable. In this paper, a model-free PID type admittance control is applied, whose parameters can be designed by human impedance properties. The inverse kinematics are required when the desired trajectories generated by admittance control in task space. It is difficult to solve the inverse kinematics problem especially when the robots are redundant, such as exoskeleton system. In this paper, we put both the upper-level PID admittance control and the lower-level linear PID control in task space. Novel sufficient conditions of semiglobal asymptotic stability are proposed via stability analysis in task space. These conditions give an explicit selection method of PID gains.

/pdf/pid-admittance-control-for-an-upper-limb-exoskeleton-4prm0yim27.pdf

Xiaoou Li

Papers

Fuzzy identification using fuzzy neural networks with stable learning algorithms

Some new results on system identification with dynamic neural networks

Support vector machine classification for large data sets via minimum enclosing ball clustering

Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework

PID admittance control for an upper limb exoskeleton