• Journal of the Semiconductor & Display Technology
• /
• v.6 no.4
• /
• pp.17-22
• /
• 2007

Vibration isolation equipments are mostly required in precise measurement and manufacturing system. Among all the vibration isolation equipments, air-spring is the most widely used equipment because of low resonant frequency and high damping ratio. In this study, we used Takagi-Sugeno fuzzy method to design an active vibration isolation system using air-spring, and compared the fuzzy method with passive control method and PID control method. Due to the non-linearity characteristics of air-spring, fuzzy controller was verified to be the most effective both in simulation and experiment.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.6
• /
• pp.5-11
• /
• 1996

This paper is the study for recognizing single vowels of speaker-independent, and we suppose a method of speech recognition using VQ(Vector Quantization)/NN(Neural Network). This method makes a VQ codebook, which is used for obtaining the observation sequence, and then claculates the probability value by comparing each codeword with the data, finally uses these probability values for the input value of the neural network. Korean signle vowels are selected for our recognition experiment, and ten male speakers pronounced eight single vowels ten times. We compare the performance of our method with those of fuzzy VQ/HMM and conventional VQ/NN According to the experiment result, the recognition rate by VQ/NN is 92.3%, by VQ/HMM using fuzzy is 93.8% and by VQ/NN using fuzzy is 95.7%. Therefore, it is shown that recognition rate of speech recognition by fuzzy VQ/NN is better than those of fuzzy VQ/HMM and conventional VQ/HMM because of its excellent learning ability.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.6
• /
• pp.415-424
• /
• 2004

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “If..., then...” statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm hell)s determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the Premise and consequence Part of the fuzzy rules. And then the initial Parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.2
• /
• pp.222-230
• /
• 2003

Once the medical knowledge-based system has been developed, it is essential to investigate the knowledge sources of the system because knowledge sources can affect the performance of the system in great deal. This paper presents the method and the results of the reliability test done on the medical knowledge-based system CLINAID. A knowledge source tested is Cardiovascular body system data used in CLINAID. The reliability test will be done by investigating structural relationships revealed by fuzzy relational method between the components of the knowledge sources of individual body systems using syndromes as its main component. These partitions are going to be compared with the syndromes elicited from the medical experts. This paper also reports the outcome of the computations using 7 implication operators performed on Cardiovascular body system data.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.18 no.2
• /
• pp.231-238
• /
• 1993

A rules of fuzzy control is to represent an expert‘s and engineer‘s ambiguous control knowledge of system with some lingustic rules. This rule is very difficult to represent perfectly because expert‘s knowledge is not precise and the rule is not perfect. We propose the fuzzy reasoning and learning to upgrade precision of imperfect rules successively after system running. In the proposed method, the precision of the backward part of a fuzzy rule is improved by back propagation learning method. Also, the method reasons the compatibility degree of the forward part of fuzzy rule by associative memory method. This method this is successfully applied to design auto-parking fuzzy controller in which expert‘s technology and knowledge are required in the limited area.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2007.11a
• /
• pp.183-189
• /
• 2007

In this paper, we developed the fuzzy group method data handling-type (GMDH) Model and applied it to demand forecasting of educational expenses. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to fuzzy system, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the fuzzy GMDH. The fuzzy GMDH-type networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the fuzzy GMDH. A computer program is developed and successful applications are shown in the field of demand forecasting problem of educational expenses with the number of factors considered.

• Proceedings of the KIEE Conference
• /
• 2000.11d
• /
• pp.791-793
• /
• 2000

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The advantage of fuzzy approach over traditional ones lies on the fact that fuzzy system does not require a detail mathematical description of the system while modeling. As modeling method. the Group Method of Data Handling(GMDH) is introduced by A.G. Ivakhnenko GMDH is an analysis technique for identifying nonlinear relationships between system's inputs and output. We study a Novel Neuro-Fuzzy Network (NNFN) in this paper. NNFN is a network resulting from the combination of a fuzzy inference system and polynomial neural network(PNN) (7) which is advanced structure of GMDH. Simulation involve a series of synthetic as well as experimental data used across various neurofuzzy systems.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.243-246
• /
• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.1149-1152
• /
• 1999

In this paper we analyzed each region of specific points and ｅ-Δｅphase plane in order to make fuzzy rule base. After we composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. The composed method are converged more rapidly than single rule base in convergence region. Proposed method is alternately use at specific points of ｅ-Δｅphase plane with two fuzzy control rules, that is one control rule occruing the steady state error used in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Two fuzzy control rules in the ｅ-Δｅ phase plane decide the change time according to response characteristics of plants. As the results of simulation through the second order plant and the delay time plan, Proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.675-678
• /
• 1997

In this paper, we present a fast and robust algorithm for the design of fuzzy controller and identifying fuzzy model from numerical data by combining the cluster estimation method with a linear least squares estimation procedure. The proposed method is compared with Adaptive Neuro-Fuzzy Inference System(ANFIS) as the standard example of neuro-fuzzy model. Finally we will show its usefulness and effectiveness for the design of fuzzy controller of a cart-pole system and fuzzy modeling for the coagulant dosing of a water purification system.