• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.12
• /
• pp.1652-1662
• /
• 1995

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.

• Proceedings of the IEEK Conference
• /
• 2002.06e
• /
• pp.261-264
• /
• 2002

In this study, we used a 16-bit microprocessor, 80C196KC for a control part in order to develop a multi-functional wheel-chair system, and implemented a joy-stick to control this system. For the complete system, we used a commercial electromotive wheelchair as a basic plant, and applied an encoder to get the rotating number of the motor to transfer data to the MCU to control the motor. We used PWM (Pulse Width Modulation) method to control the wheel-chair motor where a H-bridge circuit was configured. We used the fuzzy control algorithm for the operation of DC motor, which was attached to the electromotive wheelchair and manipulated following the change of the joystick position while a user was controlling the joystick. He also could control the speed and direction of DC motor as well as control position information.

• Proceedings of the IEEK Conference
• /
• 2002.06c
• /
• pp.111-114
• /
• 2002

This thesis introduces a buffer management method that could be used to provide better sound quality for Internet phone terminals. Proposed method used the fuzzy system for dynamically adjust the number of jitter This method actively responds to both the compression algorithms that are used by the terminals, as well as to the received data to provide an improvement in sound quality. In order to confirm the validity of the suggested algorithm, comparisons of the performance have been made between the existing buffer management method and this intelligent method in various network settings.

• International Journal of Precision Engineering and Manufacturing
• /
• v.5 no.1
• /
• pp.60-68
• /
• 2004

The purpose of this paper is to establish an effective inspection system by using OMM (On-Machine Measurement) system based PC-NC. This system can reduce manufacturing lead time because a workpiece is inspected at every machining process and the manufacturing system which includes inspection faculty is able to realize on-line process on CNC machining center. The proposed OMM system is composed of a few algorithms for determination of inspection parameters. It is accomplished by determining the number of measuring points, their location, measuring path using fuzzy logic, Hammersley's method, TSP (Traveling Salesperson Problem) algorithm. The inspection feature applied to this system is based on machining feature. This method is tested by simulation and experiment that are analyzed measuring data and geometry tolerance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1996.10a
• /
• pp.133-136
• /
• 1996

클러스터의 타당성 정도를 계산하기 위한 측정자로서, 퍼지 분할된 데이터의 서로 다른 클래스 사이의 분리성과 한 클래스안에서의 밀접성의 비율, G를 정의하였다. 본 논문에서는 이렇게 정의된 G로부터, 각 클러스터가 가지는 데이터 수의 차이점을 고려하여 하나의 데이터 집합에 대하여 서로 다른 분할들을 비교할 수 있도록 하기 위하여, IG를 재정의하였다. 기존의 클러스터 타당성 전략은 클러스터 수의 함수로서, 주어진 척도의 값을 계산하여 기록한 후 그 값의 변화가 가장 큰 경우를 최적의 클러스터의 수로서 선택하였다. 이때 그 값의 변화를 고려하기 위한 주관적인 해석이 필요하게 된다. 본 논문에서는 주관적인 해석 없이 IG를 이용하여 최적의 클러스터 수를 결정하기 위한 방법을 제안하고자 한다. 제안된 방법은 널리 알려진 Iris data와 서로 다른 클러스터 인구수를 가지는 가상의 데이터 집합에 적용하여 그 타당성을 보인다.

• Journal of KIISE:Software and Applications
• /
• v.26 no.1
• /
• pp.130-130
• /
• 1999

In this paper, we propose a novel design method for improving performance of existing FCM-type clustering algorithms. First, we define the performance measure which focuses on bothcompactness and separation of clusters. Next, we optimize this measure using evolution program.Especially the proposed method has following merits: ① using evolution program, it solves suchproblems as initialization, number of clusters, and convergence to local optimum ② it reduces searchspace and improves convergence speed of algorithm since it represents chromosome with possiblepotential centers which are selected possible candidates of centers by density measure ③ it improvesperformance of clustering algorithm with the performance index which embedded both compactnessand separation Properties ④ it is robust to noise data since it minimizes its effect on center search.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.738-741
• /
• 1995

This disseration realized the simple digital controller board using ${\mu}$-PD 70320 microprocessor has characteristics that are low cost, simple hardware organization, convenient and interchangeable with the 8086 for the servo system. We gave the control algorithm such as PD control. Self tuning adaptive control and Fuzzy control to the realized controller board and made a new real number data type for a high accuracy control. Users can select of suitable for the control algorithim. In the result of simulation and experiment shown a good performance.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.15 no.3
• /
• pp.145-152
• /
• 2015

We consider an online selective-sample learning problem for sequence classification, where the goal is to learn a predictive model using a stream of data samples whose class labels can be selectively queried by the algorithm. Given that there is a limit to the total number of queries permitted, the key issue is choosing the most informative and salient samples for their class labels to be queried. Recently, several aggressive selective-sample algorithms have been proposed under a linear model for static (non-sequential) binary classification. We extend the idea to hidden Markov models for multi-class sequence classification by introducing reasonable measures for the novelty and prediction confidence of the incoming sample with respect to the current model, on which the query decision is based. For several sequence classification datasets/tasks in online learning setups, we demonstrate the effectiveness of the proposed approach.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.5 no.1
• /
• pp.52-58
• /
• 2005

We have developed a Group Method of Data Handling (GMDH) type algorithm for designing multi-layered neural networks. The algorithm is general enough that it will accept any number of inputs and any sized training set. Each neuron of the resulting network is a function of two of the inputs to the layer. The equation for each of the neurons is a quadratic polynomial. Several forms of the equation are tested for each neuron to make sure that only the best equation of two inputs is kept. All possible combinations of two inputs to each layer are also tested. By carefully testing each resulting neuron, we have developed an algorithm to keep only the best neurons at each level. The algorithm's goal is to create as accurate a network as possible while minimizing the size of the network. Software was developed to train and simulate networks using our algorithm. Several applications were modeled using our software, and the result was that our algorithm succeeded in developing small, accurate, multi-layer networks.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.487-496
• /
• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN 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.