• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.985-992
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• 2001

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.1058-1062
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• 2003

This paper proposes a self-organizing fuzzy controller (SOFC) design technique applied to the vibration control of a dynamic system under irregular disturbance. In this controller, the fuzzy rules generate control signal continuously using the array of input and output pairs without using any special controller model. The generated rules are saved in the fuzzy rule matrix in real-time by self-organizing methods. This fuzzy logic control is demonstrated by simulation and shows the efficiency of the real-time self-organizing fuzzy controller in this system.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.322-324
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• 2005

In this paper, we introduce the advanced Self-Organizing Fuzzy Polynomial Neural Network based on optimized FPN by evolutionary algorithm and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The proposed model gives rise to a structurally and parametrically optimized network through an optimal parameters design available within Fuzzy Polynomial Neuron(FPN) by means of GA. Through the consecutive process of such structural and parametric optimization, an optimized and flexible the proposed model is generated in a dynamic fashion. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• Journal of Microbiology and Biotechnology
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• v.11 no.5
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• pp.739-748
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• 2001

A self-organizing fuzzy logic controller using a genetic algorithm is described, which controlled the glucose concentration for the enhancement of flavonoid production in a fed-batch cultivation of Scutellaria baicalensis G. plant cells. The substrate feeding strategy in a fed-batch culture was to increase the flavonoid production by using the proposed kinetic model. For the two-stage culture, the substrate feeding strategy consisted of a first period with 28 g/I of glucose to promote cell growth, followed by a second period with 5 g/I of glucose to promote flavonoid production. A simple fuzzy logic controller and the self-organizing fuzzy logic controller using a genetic algorithm was constructed to control the glucose concentration in a fed-batch culture. The designed fuzzy logic controllers were applied to maintain the glucose concentration at given set-points of the two-stage culture in fed-batch cultivation. The experimental results showed that the self-organizing fuzzy logic controller improved the controller\`s performance, compared with that of the simple fuzzy logic controller. The specific production yield and productivity of flavonoids in the two-stage culture were higher than those in the batch culture.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.15-15
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• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.1
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• pp.34-40
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• 1994

The design and implementation of a fuzzy self-organizing controller for an environmental temperature chamber is discussed. The chamber is a non-linear, time-variant system with delay-time and dead-time. And the parameter tuning is required in PI control when the performance degraded. However the proposed fuzzy-SOC monitors the performance of the process. modifies the data base, and performs the delay-time compensation based on the idealized process model. A series of experiments was performed for the conventional PI and the fuzzy-SOC. These experimental results show the usefulness of the fuzzy-SOC.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.114-117
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• 2000

In this paper, Fuzzy Self-organizing Neural Networks(FSONN) based on Fuzzy Neural Networks(FNN) is proposed to overcome some problems, such as the conflict between ovefitting and good generation, and low reliability. The proposed FSONN consists of FNN and SONN. Here, FNN is used as the premise part of FSONN and SONN is the consequnt part of FSONN. The FUN plays the preceding role of FSONN. For the fuzzy reasoning and learning method in FNN, Simplified fuzzy reasoning and backpropagation learning rule are utilized. The number of layers and the number of nodes in each layers of SONN that is based on the GMDH method are not predetermined, unlike in the case of the popular multi layer perceptron structure and can be generated. Also the partial descriptions of nodes can use various forms such as linear, modified quadratic, cubic, high-order polynomial and so on. In this paper, the optimal design procedure of the proposed FSONN is shown in each step and performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.11
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• pp.514-519
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• 2001

This Paper presents an application of a on-line self-organizing fuzzy logic controller to a boiler system of fossil-power plant. A boiler-turbine system is described as a MIMO nonlinear system in this paper. Then, three single loop fuzzy logic controllers are designed independently. The control rules and the membership functions of proposed fuzzy logic control system are generated automatically without using plant model. The simulation shows successful results for wide range operation of boiler system of fossil-power plant.

• Journal of the Korean Institute of Intelligent Systems
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• v.4 no.1
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• pp.24-34
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• 1994

A RMFSOC(Reference Model Following Self-Organizing Fuzzy Logic Controller) is propose in this paper. In the RMFSOC, the refernce model is introduced, where the desired control performance can be specified by an operator of the controlled process. The self-organizing level of the RMFSOC organizes the control rules of FLC which make the process output follow the reference model output. In addition, for the use of preventing improper modifications of control rules, a complementary decission rule is induced from the possible relations between the process output and reference model output. Through a simulation study, it is shown that the robustness of the control system using the proposed RMFSOC to the set-point changes and distur bances can be greatly improved being conpared with that of the control system using the Procyk and Mamdani's SOC.