• Journal of the Korea Concrete Institute
• /
• v.14 no.6
• /
• pp.1022-1031
• /
• 2002

This paper presents efficient models for bridge structures using CART-ANFIS (classification and regression tree-adaptive neuro fuzzy inference system). A fuzzy decision tree partitions the input space of a data set into mutually exclusive regions, each region is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it continuous and smooth everywhere. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Proceedings of the KIEE Conference
• /
• 2008.04a
• /
• pp.151-152
• /
• 2008

본 논문에서는 도립 역진자 시스템에서의 진자의 도립 상태를 유지하도록 하기 위하여, DSP와 FPGA를 결합하여 ANFIS 뉴로퍼지 제어기를 구현하여 실험하였다. 도립진자의 위치 추종 성능을 PID 제어기와 비교 평가하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.46-50
• /
• 2001

Voice is one of the most efficient communication media and it includes several kinds of factors about speaker, context emotion and so on. Human emotion is expressed in the speech, the gesture, the physiological phenomena (the breath, the beating of the pulse, etc). In this paper, the method to have cognizance of emotion from anyone's voice signals is presented and simulated by using neuro-fuzzy model.

• International Journal of Precision Engineering and Manufacturing
• /
• v.3 no.4
• /
• pp.24-30
• /
• 2002

In grinding operations, one of the most important problems is to increase efficiency of process. In order to achieve this purpose, it is necessary to administer the tool lift of grinding wheel and to optimize grinding conditions. Frequently dressing result in lowering the process efficiency remarkably and makes production cost high. On the other hand, grinding with a worn wheel causes the workpiece surface roughness to increase and often results in the occurrence of such troubles as chatter vibration and homing.

• Proceedings of the KAIS Fall Conference
• /
• 2007.05a
• /
• pp.150-153
• /
• 2007

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• Proceedings of the Korean Environmental Health Society Conference
• /
• 2003.06a
• /
• pp.147-149
• /
• 2003

Automatic monitoring and controling system, especially DO and MLSS was faciliated for the nitrogen and phosphorus removal efficiencies. Removal efficiency of nitrogen and phosphorus by automatic monitoring and controling system, especially DO and SRT was have well adopted. and so it will be possible to use artificial intelligence logic control software such as fuzzy or neuro logic control system for WWT Plant.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.04a
• /
• pp.567-570
• /
• 2004

기존의 엘리베이터 시스템은 모든 교통 상황에 대해서 고정된 속도 패턴을 사용함으로써 교통량 변화에 다양한 속도 패턴을 제공 할 수 없었다. 운송 속도와 승차감은 엘리베이터 속도 패턴을 결정하기 위한 두개의 중요한 요소이다. 기동과 정지 시에 변속 충격을 줄이기 위해서 가속과 감속 시간이 적절히 조정되어졌다. 운송능력을 향상시키기 위해서 교통량 변화에 맞추어 저크를 조정하였고 이와 같은 방법으로 6개의 속도 패턴 곡선과 엘리베이터의 속도 제어를 위해서 뉴로 퍼지 시스템을 구현하였다. 구현된 뉴로 퍼지 시스템은 2개의 입력변수와 1개의 출력을 가진 시스템이다. 전반부는 교통량의 변화를 나타내며 후반부는 입력에 대응되는 속도 패턴을 적용시켰다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.439-440
• /
• 2004

전력시스템의 부하주파수제어는 전력계통운용에 있어서 가장 중요하게 다루어야 한다. 본 논문에서는 강인한 퍼지제어기를 얻고자, 다층 신경회로망을 이용하여 퍼지제어기 멤버쉽 함수의 전건부 및 후건부 파라미터들을 시스템에 알맞게 자기 조정하기 위해 최급구배법에 근거한 오차 역전파 알고리즘으로 적응 학습시킬 수 있는 뉴로-퍼지제어기의 구조 및 알고리즘을 제안하였다.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.46.3-46
• /
• 2001

The overall aim of this research is to develop a computer communication interface based on brain-wave bio potentials for physically disabled people. The work focuses on using EOG and EMG signals to input characters one by one using cursor movements on a GUI screen. The Cyberlink TM system is used to acquire brain waves in real time with electrodes. EMG and EOG signals are used to direct a cursor in order to select, or to click on a character on the screen. We present a novel method for automatic EOG pattern detection by using wavelet transforms with a neuro-fuzzy approach ...

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.102.1-102
• /
• 2002

In this paper, we propose Self-organizing neurofuzzy networks(SONFN) and discuss their comprehensive design methodology. The proposed SONFN is generated from the mutually combined structure of both neurofuzzy networks (NFN) and polynomial neural networks(PNN) for model identification of complex and nonlinear systems. NFN contributes to the formation of the premise part of the SONFN. The consequence part of the SONFN is designed using PNN. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. We discuss two kinds of SONFN architectures and propose a comprehensive learning algorithm. It is shown that this network...