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

/pdf/a-survey-on-analysis-and-design-of-model-based-fuzzy-control-3hrjlrel0k.pdf

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

Fuzzy Petri net (FPN) theory can be used as a computational paradigm for intelligent systems. This specification and engineering language provides a graphical tool for visualization, communication and interpretation, and supports to manipulate imprecise and vague information. However, it is lack of adjustment (learning) mechanism being proposed to cope with potential numerical deficiencies of these models, and to adapt system inconstancy. This paper proposes a weighted approach which can overcome this shortage. A new FPN model with adaptive weights is proposed for knowledge learning and reasoning. Based on this model, fuzzy knowledge reasoning and weight learning algorithms are developed.

A weighted fuzzy petri net model for knowledge learning and reasoning

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence. In this paper, Kalman filter is modified with a risk-sensitive cost criterion, we call it as risk-sensitive Kalman filter. This new algorithm is applied to train recurrent neural networks for nonlinear system identification. Input-to-state stability is used to prove that the risk-sensitive Kalman filter training is stable. The contributions of this paper are: 1) the risk-sensitive Kalman filter is used for the state-space recurrent neural networks training, 2) the stability of the risk-sensitive Kalman filter is proved.

Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm

Since knowledge is vague and modified frequently in a expert system, this kind of rule-based 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. This paper presents an Adaptive Fuzzy Petri Net with Triangular function model (AFPNT). The fuzzy production rules in the rule-based system are modeled by AFPNT. Just as other fuzzy Petri net, AFPNT can be used for knowledge representation and reasoning. But AFPNT has one important advantage: it is suitable for vague and dynamic knowledge, i.e., the fuzzy model are adjustable by the data or the knowledge. Based on transition firing rules, a modification back propagation learning algorithm is developed for AFPNT to assure the convergence of the weights.

Fuzzy Knowledge Learning via Adaptive Fuzzy Petri Net with Triangular Function Model

Currently, vertical partitioning has been used in multimedia databases in order to take advantage of its potential benefits in query optimization. Nevertheless, most vertical partitioning algorithms are static; this means that they optimize a vertical partitioning scheme (VPS) according to a workload, but if this workload suffers changes, the VPS may be degraded, which would result in long query response time. This paper presents a set of active rules to perform dynamic vertical partitioning in multimedia databases. First of all, these rules collect all the information that a vertical partitioning algorithm needs as input. Then, they evaluate this information in order to know if the database has experienced enough changes to trigger a performance evaluator. In this case, if the performance of the database falls below a threshold previously calculated by the rules, the vertical partitioning algorithm is triggered, which gets a new VPS. Finally, the rules materialize the new VPS. Our active rule base is implemented in the system DYMOND, which is an active rule-based system for dynamic vertical partitioning of multimedia databases. DYMOND's architecture and workflow are presented in this paper. Moreover, a case study is used to clarify and evaluate the functionality of our active rule base. Additionally, authors of this paper performed a qualitative evaluation with the aim of comparing and evaluating DYMOND's functionality. The results showed that DYMOND improved query performance in multimedia databases.

Active rule base development for dynamic vertical partitioning of multimedia databases

Support Vector Machines (SVM) have gained profound interest amidst the researchers. One of the important issues concerning SVM is with its application to large data sets. It is recognized that SVM is computationally very intensive. This paper presents a novel multi SVM classification approach for large data sets using the sketch of classes distribution which is obtained by using SVM and minimum enclosing ball (MEB) method. Our approach has distinctive advantages on dealing with huge data sets. Experiments done with several large synthetic and real world data sets, show good performance on computational expense and accuracy. keywords: support vector machines, multi-classification, large data sets.

Xiaoou Li

Papers

A weighted fuzzy petri net model for knowledge learning and reasoning

Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm

Fuzzy Knowledge Learning via Adaptive Fuzzy Petri Net with Triangular Function Model

Active rule base development for dynamic vertical partitioning of multimedia databases

Multi-class SVM for large data sets considering models of classes distribution