Nai Ren Guo

Bio: Nai Ren Guo is an academic researcher from National Cheng Kung University. The author has contributed to research in topics: Fuzzy classification & Fuzzy set operations. The author has an hindex of 6, co-authored 7 publications receiving 159 citations. Previous affiliations of Nai Ren Guo include Tung Fang Design Institute.

Design of a Novel Fuzzy Sliding-Mode Control for Magnetic Ball Levitation System

Abstract: This paper presents the design of a novel fuzzy sliding-mode control (NFSMC) for the magnetic ball levitation system. At first, we examine the nonlinear dynamic models of the magnetic ball system, where the singular perturbation method is used. Next, we address the design schemes of sliding mode control (SMC) and traditional fuzzy sliding-mode control (FSMC), where two kinds of FSMCs are introduced. Then we provide the design steps of the NFSMC, where the Lyapunov stability analysis is also given. Finally, a magnetic ball levitation system is used to illustrate the effectiveness of the proposed controller.

Design of an EP-based fuzzy sliding-mode control for a magnetic ball suspension system

Abstract: In this paper, an evolutionary programming (EP) based fuzzy sliding-mode control (FSMC) is proposed for a magnetic ball suspension system. Firstly, we apply a singular perturbation method to examine a nonlinear model proposed for the magnetic ball suspension system. Secondly, we address design methods of sliding-mode control (SMC), FSMC, and dynamic FSMC. We utilize the EP to adjust scaling factors of the input and output variables for the FSMC. Global asymptotic stability of the EP-based FSMC is confirmed by the Lyapunov stability theory. Finally, simulations on the magnetic ball suspension system are given to show the tracking performances of the proposed control structure.

Construction of a neuron-fuzzy classification model based on feature-extraction approach

Abstract: In this paper, a Feature-Extraction Neuron-Fuzzy Classification Model (FENFCM) is proposed that enables the extraction of feature variables and provides the classification results. The proposed classification model synergistically integrates a standard fuzzy inference system and a neural network with supervised learning. The FENFCM automatically generates the fuzzy rules from the numerical data and triangular functions that are used as membership functions both in the feature extraction unit and in the inference unit. To adapt the proposed FENFCM, two modificatory algorithms are applied. First, we utilize Evolutionary Programming (EP) to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the Weight Revised Algorithm (WRA) is used to regulate the weight grade of the principal output node of the inference unit. Finally, the proposed FENFCM is validated using two benchmark data sets: the Wine database and the Iris database. Computer simulation results demonstrate that the proposed classification model can provide a sufficiently high classification rate in comparison with that of other models proposed in the literature.

Design of a two-stage fuzzy classification model

Abstract: This paper proposes a novel two-stage fuzzy classification model established by the fuzzy feature extraction agent (FFEA) and the fuzzy classification unit (FCU). At first, we propose a FFEA to validly extraction the feature variables from the original database. And then, the FCU, which is the main determination of the classification result, is developed to generate the if-then rules automatically. In fact, both the FFEA and FCU are fuzzy models themselves. In order to obtain better classification results, we utilize the genetic algorithms (GAs) and adaptive grade mechanism (AGM) to tune the FFEA and FCU, respectively, to improve the performance of the proposed fuzzy classification model. In this model, GAs are used to determine the distribution of the fuzzy sets for each feature variable of the FFEA, and the AGM is developed to regulate the confidence grade of the principal if-then rule of the FCU. Finally, the well-known Iris, Wine, and Glass databases are exploited to test the performances. Computer simulation results demonstrate that the proposed fuzzy classification model can provide a sufficiently high classification rate in comparison with other models in the literature.

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Abstract: Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.

Chattering-free fuzzy sliding-mode control strategy for uncertain chaotic systems

Abstract: This paper proposes a chattering-free fuzzy sliding-mode control (FSMC) strategy for uncertain chaotic systems. A fuzzy logic control is used to replace the discontinuous sign function of the reaching law in traditional sliding-mode control (SMC), and hence a control input without chattering is obtained in the chaotic systems with uncertainties. Base on the Lyapunov stability theory, we address the design schemes of integration fuzzy sliding-mode control, where the reaching law is proposed by a set of linguistic rules and the control input is chattering free. The Genesio chaotic system is used to test the proposed control strategy and the simulation results show the FSMC not only can control the uncertain chaotic behaviors to a desired state without oscillator very fast, but also the switching function is smooth without chattering. This result implies that this strategy is feasible and effective for chaos control.

Chaos synchronization using fuzzy logic controller

Abstract: The design of a rule-based controller for a class of master-slave chaos synchronization is presented in this paper. In traditional fuzzy logic control (FLC) design, it takes a long time to obtain the membership functions and rule base by trial-and-error tuning. To cope with this problem, we directly construct the fuzzy rules subject to a common Lyapunov function such that the master–slave chaos systems satisfy stability in the Lyapunov sense. Unlike conventional approaches, the resulting control law has less maximum magnitude of the instantaneous control command and it can reduce the actuator saturation phenomenon in real physic system. Two examples of Duffing–Holmes system and Lorenz system are presented to illustrate the effectiveness of the proposed controller.