• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.405-406
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• 2007

본 논문에서는 FCM 기반 퍼지 뉴럴네트워크 구조를 제안하고 진화 알고리즘을 이용한 FCM 기반 퍼지 뉴럴네트워크의 구조와 파라미터의 최적화 방법을 제시한다. 클러스터링 알고리즘은 퍼지 뉴럴 네트워크에서 멤버쉽함수의 중심점과 반경 등을 결정하는 학습에 일반적으로 사용된다. 제안된 FCM 기반 뉴럴 네트워크에서 멤버쉽함수는 가우시안, 삼각형 타입등의 정해진 형태를 사용하지 않고 데이터들 사이의 거리에 관계된 계산을 수행하는 FCM에 의해 결정된다. 후반부는 상수형, 선형, 2차식 등의 다양한 다항식 구조로 표현될 수 있으며 다항식의 계수는 LSE를 이용하여 결정한다. FCM 기반 퍼지 뉴럴 네트워크는 퍼지규칙의 수, 입력변수의 선택, 후반부 다항식의 차수, FCM의 퍼지화 계수의 결정은 성능에 많은 차이가 있으며 이러한 구조와 파라미터의 최적화가 요구된다. 본 논문에서는 유전자 알고리즘을 이용하여 FCM 기반 퍼지뉴럴네트워크의 구조에 관련된 입력변수의 수, 퍼지규칙의 수 그리고 후반부 다항식의 차수와 파라미터에 관련된 퍼지화 계수를 최적화 한다. 제안된 방법은 비선형 시스템의 모델링에 적용하여 성능을 분석하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.472-476
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• 2007

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.202-214
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• 2002

In this paper, we propose a modified FCM (MFCM) algorithm to solve the problems of the FCM algorithm and the fuzzy clustering algorithm using an average intracluster distance (FCAID). The MFCM algorithm grants the regular grade of membership in the small size of cluster. And it clears up the convergence problem of objective function because its objective function is designed according to the grade of membership of it, verified, and used for clustering data. So, it can solve the problem of the FCM algorithm in different size of cluster and the FCAID algorithm in the convergence problem of objective function. To verify the MFCM algorithm, we compared with the result of the FCM and the FCAID algorithm in data clustering. From the experimental results, the MFCM algorithm has a good performance compared with others by classification entropy.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.304-309
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• 2013

This paper proposes a intelligent approach on range decision of figure of merit. Unknown range of the underwater target and the non-fixed signal excess make the uncertainty for the tracking process. Using the input data of signal excess related to the range, we establish the rule of the fuzzy set and the original data acquired by sonar can be transformed to the fuzzified data set. To reduce the error arisen from the unexpected data, we use the new data transformed in fuzzy set. The piecewise relations of the min value, max one, and the mean one are calculated. The three values are used for the expected range of the underwater target. By analysing the fluctuation of the data, we can expect the target's position and the characteristics of the maneuvering. The examples are presented to show the performance and the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.163-168
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• 2008

Navigation system based on indoor location estimation is one of the core technologies in mobile robot systems. Wireless sensor network has great potential in the indoor location estimation due to its characteristics such as low power consumption, low cost, and simplicity. In this paper we present an algorithm to estimate the indoor location of mobile robot based on wireless sensor network and fuzzy modeling. ZigBee-based sensor network usually uses RSSI(Received Signal Strength Indication) values to measure the distance between two sensor nodes, which are affected by signal distortion, reflection, channel fading, and path loss. Therefore we need a proper correction method to obtain accurate distance information with RSSI. We develop the fuzzy distance models based on RSSI values and an efficient algorithm to estimate the robot location which applies to the navigation algorithm incorporating the time-varying data of environmental conditions which are received from the wireless sensor network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.60-63
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• 2008

본 논문에서는 신경망 모델(neural network model)과 유전자 알고리즘(genetic algorithm)을 이용한 실시간 경로 계획(real-time path-planning)과 퍼지 룰(fuzzy rule)을 이용한 효율적인 다중경로계획(multiple path-planning)을 제안한다. 실시간 경로 계획은 빠른 시간 내에 최적 경로의 생성이 반드시 수행되어야 하므로, 본 논문에서는 경로 계획 중 장애물 지역과 비장애물 지역을 빠르게 확인하기 위해 신경망 모델을 이용하여, 이동 방향 및 최적경로 탐색을 위하여 유전자 알고리즘을 이용하였다. 또한 충돌 구역에서의 효율적인 다중 경로 계획을 위해, 퍼지를 이용하여 경로를 재계획 하였다. 퍼지의 경우, 현재 위치에서 목표 지점으로의 방향을 계산하기 위한 퍼지 소속 함수와 현재 위치와 충돌 구역까지의 거리 값을 가중치로 세우고 퍼지 룰을 결정하여 경로계획을 수행하였다. 시뮬레이션을 통해 실험해본 결과, 퍼지 룰을 사용했을 때 사용하지 않았을 때 보다 좋은 성능을 나타남을 확인할 수 있었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.198-204
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• 2012

Tracking and recognition of robots are required for the cooperation task of robots in various environments. In the paper, a tracking control system of moving robot using stereo image processing, code-book model and fuzzy controller is proposed. First, foreground and background images are separated by using code-book model method. A candidate region is selected based on the color information in the separated foreground image and real distance of the robot is estimated from matching process of depth image that is acquired through stereo image processing. The open and close processing of image are applied and labeling according to the size of mobile robot is used to recognize the moving robot effectively. A fuzzy tracking controller using distance information and mobile information by stereo image processing is designed for effective tracking according to the movement velocity of the target robot. The proposed fuzzy tracking control method is verified through tracking experiments of mobile robots with stereo camera.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.901-906
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• 2007

Algorithms of path planning and obstacle avoidance are essential to autonomous mobile robots that are working in unknown environments in the real time. This paper presents a new navigation algorithm for an autonomous mobile robot with vision and IR sensors using fuzzy rules. Temporary targets are set up by distance variation method and then the algorithms of trajectory planning and obstacle avoidance are designed using fuzzy rules. In this approach, several digital image processing technique is employed to detect edge of obstacles and the distances between the mobile robot and the obstacles are measured. An autonomous mobile robot with single vision and IR sensors is built up for experiments. We also show that the autonomous mobile robot with the proposed algorithm is navigating very well in complex unknown environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.460-465
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• 2006

This paper presents a new fuzzy learning rule which considers the Euclidean distances between the input vector and the prototypes of classes. The new fuzzy learning rule is integrated into the supervised IAFC neural network 4. This neural network is stable and plastic. We used iris data to compare the performance of the supervised IAFC neural network 4 with the performances of back propagation neural network and LVQ algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.359-362
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• 2004

본 논문에서는 평균내부거리를 적용한 퍼지 클러스터링 알고리즘과 클러스터들 사이의 중첩성을 이용한 위성영상의 분류 알고리즘을 제안하였다. 제안된 방법은 클러스터의 크기에 따라 큰 클러스터에는 많은 소속정도를 작은 클러스터에는 적은 소속정도를 부여함으로 크기가 다른 클러스터가 존재하는 데이터 집합에 대해서도 분류의 효율성을 높였다. 클러스터들간의 중첩성을 이용한 평가를 통해 위성영상에 있어서 중첩되지 않은 화소는 각각의 분류항목에 포함시키고, 중첩된 화소들은 최대우도 분류를 수행한 후 각 화소에 대한 우도와 퍼지 클러스터링의 소속도를 비교 분석하여 최종 분류항목을 결정함으로서 분류를 효율적으로 할 수 있다.