• 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 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.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1086-1088
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• 1993

A robust fuzzy self-organizing controller(SOC) is proposed for an environmental temperature chamber. Although fuzzy SOC can improve the performance of nonlinear system, the controller is ineffective to solve the performance degradation owing to the time varying factors. In this paper, we construct the fuzzy SOC with a predictive scheme based on the 386PC. The usefulness of the proposed scheme is shown through the comparison of the PI controller and the fuzzy controller.

• Journal of the Korean Society of Safety
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• v.12 no.1
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• pp.129-132
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• 1997

The effectiveness of an ensuring the facility safety depends on the ability to find abnormal part(s) and remove that part(s). This requires the knowledge of that machine and ability to recover that machine. In this paper, it is discribed how to design the fuzzy based self organizing neural network expert system in order to find syptom source(s).

• Atmosphere
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• v.21 no.3
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• pp.221-228
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• 2011

The nonlinear characteristics of summer monsoon intraseasonal oscillation (ISO) in precipitation, which is manifested as fluctuations in convection and circulation, is one of the major difficulty on the prediction of East Asian summer monsoon (EASM). The present study aims to identify the spatial distribution and time evolution of nonlinear phases of monsoon ISO. In order to classify the different phases of monsoon ISO, Self-Organizing Map(SOM) known as a nonlinear pattern recognition technique is used. SOM has a great attractiveness detecting self-similarity among data elements by grouping and clustering such self-similar components. The four important patterns are demonstrated as Meiyu-Baiu, Changma, post-Changma, and dry-spell modes. It is found that SOM well captured the formation of East Asian monsoon trough during early summer and its northward migration together with enhanced convection over subtropical western Pacific and regionally intensive precipitation including Meiyu, Changma and Baiu. The classification of fundamental large scale spatial pattern and evolutionary history of nonlinear phases of monsoon ISO provides the source of predictability for extended-range forecast of summer precipitation.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.21-34
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• 1998

[n this paper, we propose a hierarchical self-organizing fuzzy logic controller to improve the performance of the FARMA(Fuzzy auto-regressive moving average) SOC(Self-organizing fuzzy logic controller) when the system parameters change. The proposed controller contains the FARMA SC)C in the lower level and has a coordinator in the higher level, which evaluates convergence. and when it senses the degradation of system performance it compensates the control input by a look-up table. The proposed controller shows good perforniance over the FARMA SOC when the system parameters change. We executed some computer simulations on the regulation problem of an inlrerted pendulum system and compared the results with those of the FARMA SOC. As a result, it ha:; been shown that the proposed controller outperformed the FARMA SOC when the changes of the system parameters occurred.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.21-33
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• 1997

In this paper, a method of self-organizing for fuzzy logic controller(FLC) through learning of the proper direction of coritrol is proposed. In case of designing a self-organizing FLC for unknown dynamic plants based on the gradient descent method, it is difficult to identify the desirable direction of the change of control inpul. in which the error would be decreased. To resolve this problem, we propose a method as fo1lows:at first, assign representative values for the direction of change of error with respect to control input to each partitioned region of the states, and then, learn the fuzzy control rules using the reinforced representative values through iterative trials. 'The proposed self-organizing FLC has simple structure and it is easy to design. The validity of the proposed method is proved by the computer simulation for an inverted pendulum system.

• Journal of Communications and Networks
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• v.13 no.4
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• pp.367-376
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• 2011

In this paper, we propose self-organizing schemes for the initial configuration of the neighbor cell list (NCL), maintenance of the NCL, and physical cell identifier (PCI) allocation in heterogeneous networks such as long term evolution systems where lower transmission power nodes are additionally deployed in macrocell networks. Accurate NCL maintenance is required for efficient PCI allocation and for avoiding handover delay and redundantly increased system overhead. Proposed self-organizing schemes for the initial NCL configuration and PCI allocation are based on evolved universal terrestrial radio access network NodeB (eNB) scanning that measures reference signal to interference and noise ratio and reference symbol received power, respectively, transmitted from adjacent eNBs. On the other hand, the maintenance of the NCL is managed by adding or removing cells based on periodic user equipment measurements. We provide performance analysis of the proposed schemes under various scenarios in the respects of NCL detection probability, NCL false alarm rate, handover delay area ratio, PCI conflict ratio, etc.

• Journal of the Korean Society for information Management
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• v.24 no.3
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• pp.321-341
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• 2007

It will be possible to prevent the infringement of the trademarks and the insueing disputes regarding the originality of the trademarks by using an efficient content-based trademark image retrieval system. In this paper, we describe a content-based image retrieval system using the Self Organizing Map(SOM) algorithm. The SOM algorithm utilizes the visual features, which were derived from the gray histogram representation of the images. In addition, we made the objective effectiveness evaluation possible by coming up with a quantitative measure to gauge the effectiveness of the content-based image retrieval system.