• Journal of Mechanical Science and Technology
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• v.17 no.1
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• pp.105-113
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• 2003

This paper proposes a systematic design methodology for the Takagi-Sugeno (TS) model based fuzzy state feedback control system with multi-objectives. In this investigation, the objectives are set to be guaranteed stability and pre-specified transient performance, and this scheme is applied to a nonlinear magnetic bearing system. More significantly, in the proposed methodology, the control design problems that consider both stability and desired transient performance are reduced to the standard LMI problems. Therefore, solving these LMI constraints directly (not trial and error) lead to a fuzzy state-feedback controller such that the resulting fuzzy control system meets the above two objectives. Simulation and experimentation results show that the Proposed LMI-based design methodology yields not only maximized stability boundary but also the desired transient responses.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.189-192
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• 1996

This paper presents a systematic approach to identify a linguistic fuzzy model for a multi-input and single-output complex system. Such a model is composed of fuzzy rules, and its output is inferred by the simplified reasoning. The structure and membership function parameters for a fuzzy model are automatically and simultaneously identified by GA (Genetic Algorithm). After GA search, optimal parameters for the fuzzy model are finely tuned by a gradient method. A numerical example is provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce the linguistic fuzzy model with higher accuracy and a smaller number of rules than the ones achieved previously in other methods.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.473-518
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• 2011

Software measurement is a key factor in managing, controlling, and improving the software development processes. Software quality is one of the most important factors for assessing the global competitive position of any software company. Thus the quantification of quality parameters and integrating them into quality models is very essential. Software quality criteria are not very easily measured and quantified. Many attempts have been made to exactly quantify the software quality parameters using various models such as ISO/IEC 9126 Quality Model, Boehm's Model, McCall's model, etc. In this paper an attempt has been made to provide a tool for precisely quantifying software quality factors with the help of quality factors stated in ISO/IEC 9126 model. Due to the unpredictable nature of the software quality attributes, the fuzzy multi criteria approach has been used to evolve the quality of the software.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.43-50
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• 1997

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. This makes it very dificult to apply conventional control methods to the combustion control of the refuse. So most of the refuse incineration plant are operated by operators. In this paper, an multi-variable fuzzy model predictive controller is proposed for the combustion control of the re:fuse. Adaptive network based fuzzy inference system is used for modeling of the refuse incineration plant and multi-variable fuzzy model predictive controller is designed based on the identified fuzzy model. And computer simulation was carried out to evaluate performance of the proposed controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.8
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• pp.423-431
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• 2000

This paper presents a systematic approach to the input-output data-based fuzzy modeling for the complex and uncertain nonlinear systems, in which the conventional mathematical models may fail to give the satisfying results. To do this, we propose a new method that can yield a successful fuzzy model using a mGA hybrid schemes with a fine-tuning method. We also propose a new coding method fo chromosome for applying the mGA to the structure and parameter identifications of fuzzy model simultaneously. During mGA search, multi-purpose fitness function with a penalty process is proposed and adapted to guarantee the accurate and valid fuzzy modes. This coding scheme can effectively represent the zero-order Takagi-Sugeno fuzzy model. The proposed mGA hybrid schemes can coarsely optimize the structure and the parameters of the fuzzy inference system, and then fine tune the identified fuzzy model by using the gradient descent method. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its applications to two nonlinear systems.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.939-944
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• 2002

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.33-38
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• 2006

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The conventional FPNN developed so far are based on mechanisms of self-organization and evolutionary optimization. The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed advanced genetic algorithms based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.361-366
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• 2001

This thesis proposes a fuzzy logic cross coupled controller for a multi axis servo system. The overall control system consists of three elements: the axial position controller, the speed controller, and a fuzzy logic cross coupled controller. In conventional multi axis servo system, the motion of each axis is controlled independently without regard to the motion of other axes, in which the contour error, defined as the shortest distance between the desired and actual contours is compensated only by the position error of each axis. This decoupled control approach may result in degraded contouring performance due to such factors as mismatch of axial dynamics and axial loop gains. In practice, such systems contain many uncertainties, Therefore, the multi axis servo system must receive and evaluate the motion of all axes for a better contouring accuracy. Cross coupled controller utilizes all axis position error information simultaneously to produce accurate contours. However the existing cross coupled controllers cannot overcome friction, backlash and parameter variation. Also, since it is difficult to obtain an accurate mathematical model of multi axis system, here we investigate a fuzzy logic cross coupled controller method. Some simulations and experimental results are presented to illustrate the performance of the proposed controller.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1009-1011
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• 1996

This paper presents an approach to building multi-input and single-output fuzzy models for nonlinear data-based systems. Such a model is composed of fuzzy rules, and its output is inferred by simplified reasoning. Optimal structure and membership parameters for a fuzzy model are automatically and simultaneously identified by GA(Genetic Algorithm). Numerical examples are provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce a fuzzy model with higher accuracy and a smaller number of fuzzy rules than the ones achieved previously in other methods.