• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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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.

• 한국지능시스템학회논문지
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• 제4권1호
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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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• 제어로봇시스템학회 2003년도 ICCAS
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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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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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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.

• 한국안전학회지
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• 제12권1호
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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).

• 대기
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• 제21권3호
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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.

• 한국지능시스템학회논문지
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• 제8권2호
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• pp.21-34
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• 1998

본 논문에서는 시스템 파라미터의 변호가 있는 경우에 수렴성이 떨어지는 FARMA 자기구성 퍼지제어기의 단점을 보완하기 위하여 수렴성을 평가하여 제어입력을 보상하는 상위 계층이 있는 자기구성 퍼지제어기를 제안한다. 제안한 방법은 하위계층에서 FARMA 자기구성퍼지제어기, 상위계층에서는 조정자가 시스템의 수렴성을 평가하여 수렴성이 저하되는 것이 감지되면 출력 오차와 오차 변화율에 따라 구성된 보상표에 의하여 보상 제어입력을 구해 더해주므로써 수렴성을 개선시키도록 하였다. 도립진자 시스템에서 시스템 파라미터의 변화가 있는 경우에 대해 컴퓨터모사한 결과 제안한 방법이 FARMA 자기구성 퍼지제어기에 비하여 응답특성이 개선됨을 알 수 있었다.

• 한국지능시스템학회논문지
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• 제7권3호
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• pp.21-33
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• 1997

본 논문에서는 바람직한 제어 방향의 학습을 통한 퍼지 제어기의 새로운 자기 구성 방법을 제안한다. 기울기 강하법에 기반하여 특성을 모르는 동적 플랜트에 대한 퍼지 제어기를 자기 구성할 때 풀어야할 문제중 하나는 오차를 줄이도록 하는 바람직한 제어입력의 변화방향을 알아내는 것이다. 이 문제를 해결하기 위한 방법으로서, 제어입력에 따른 오차의 변화 방향에 대한 대표 값을 분할된 상태영역에 할당하고, 반복적인 시행을 통해 강화 학습된 이 대표값을 이용하여 퍼지 제어 규칙을 학습하는 방법을 제안하였다. 제안된 자기구성 퍼지제어기는 간단한 구조를 가질 뿐 아니라 설계하기도 쉬운 장점을 갖는다. 제안된 방법의 타당성은 역진자 시스템에 대한 모의 실험을 통하여 검증하였다.

• Journal of Communications and Networks
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• 제13권4호
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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.

• 정보관리학회지
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• 제24권3호
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• pp.321-341
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• 2007

산업재산권중 하나인 상표에 대한 효율적인 이미지 검색은 상표도용 및 이로 인한 분쟁을 방지할 수 있다. 이를 위해서 효율적인 내용기반 유사이미지 검색이 하나의 방안이 될 수 있다. 본 논문은 상표이미지로부터 회색조 히스토그램(gray histogram) 분석을 통하여 가시적인 자질을 추출하여 Self Organizing Map(SOM) 알고리즘을 적용한 내용기반 유사이미지 검색시스템을 이용하는 방법을 제안하였다. 또한 내용기반 유사이미지 검색시스템의 정량적인 성능평가 방안을 제시하여 본 연구에서 개발한 이미지 검색 시스템의 객관적인 성능평가를 수행하였다.