• 제목/요약/키워드: polynomial networks

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Design of Sliding Mode Fuzzy Controller for Vibration Reduction of Large Structures (대형구조물의 진동 감소를 위한 슬라이딩 모드 퍼지 제어기의 설계)

  • 윤정방;김상범
    • Journal of the Earthquake Engineering Society of Korea
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    • 제3권3호
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    • pp.63-74
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    • 1999
  • A sliding mode fuzzy control (SMFC) algorithm is presented for vibration of large structures. Rule-base of the fuzzy inference engine is constructed based on the sliding mode control, which is one of the nonlinear control algorithms. Fuzziness of the controller makes the control system robust against the uncertainties in the system parameters and the input excitation. Non-linearity of the control rule makes the controller more effective than linear controllers. Design procedure based on the present fuzzy control is more convenient than those of the conventional algorithms based on complex mathematical analysis, such as linear quadratic regulator and sliding mode control(SMC). Robustness of presented controller is illustrated by examining the loop transfer function. For verification of the present algorithm, a numerical study is carried out on the benchmark problem initiated by the ASCE Committee on Structural Control. To achieve a high level of realism, various aspects are considered such as actuator-structure interaction, modeling error, sensor noise, actuator time delay, precision of the A/D and D/A converters, magnitude of control force, and order of control model. Performance of the SMFC is examined in comparison with those of other control algorithms such as $H_{mixed 2/{\infty}}$ optimal polynomial control, neural networks control, and SMC, which were reported by other researchers. The results indicate that the present SMFC is an efficient and attractive control method, since the vibration responses of the structure can be reduced very effectively and the design procedure is simple and convenient.

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Design of RBFNN-based Emotional Lighting System Using RGBW LED (RGBW LED 이용한 RBFNN 기반 감성조명 시스템 설계)

  • Lim, Sung-Joon;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • 제62권5호
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    • pp.696-704
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    • 2013
  • In this paper, we introduce the LED emotional lighting system realized with the aid of both intelligent algorithm and RGB LED combined with White LED. Generally, the illumination is known as a design factor to form the living place that affects human's emotion and action in the light- space as well as the purpose to light up the specific space. The LED emotional lighting system that can express emotional atmosphere as well as control the quantity of light is designed by using both RGB LED to form the emotional mood and W LED to get sufficient amount of light. RBFNNs is used as the intelligent algorithm and the network model designed with the aid of LED control parameters (viz. color coordinates (x and y) related to color temperature, and lux as inputs, RGBW current as output) plays an important role to build up the LED emotional lighting system for obtaining appropriate color space. Unlike conventional RBFNNs, Fuzzy C-Means(FCM) clustering method is used to obtain the fitness values of the receptive function, and the connection weights of the consequence part of networks are expressed by polynomial functions. Also, the parameters of RBFNN model are optimized by using PSO(Particle Swarm Optimization). The proposed LED emotional lighting can save the energy by using the LED light source and improve the ability to work as well as to learn by making an adequate mood under diverse surrounding conditions.

Design of Optimized pRBFNNs-based Night Vision Face Recognition System Using PCA Algorithm (PCA알고리즘을 이용한 최적 pRBFNNs 기반 나이트비전 얼굴인식 시스템 설계)

  • Oh, Sung-Kwun;Jang, Byoung-Hee
    • Journal of the Institute of Electronics and Information Engineers
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    • 제50권1호
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    • pp.225-231
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    • 2013
  • In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

A Joint Resource Allocation and Routing Scheme for the IEEE 802.16j Multi-hop Relay Networks (IEEE 802.16j 멀티홉 릴레이 네트워크를 위한 통합 자원 할당-라우팅 기법)

  • Lee, Kyung-Joo;Lee, Hyuk-Joon
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • 제8권1호
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    • pp.82-91
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    • 2009
  • Routing (or path selection) is one of the key issues of multi-hop relay networks such as the IEEE 802.16j. Moreover, the allocation of appropriate resource such as bandwidth should not only be made in accordance with the paths selected, but the utilization of radio resource of an entire cell should also be maximized. Due to this interdependency between the problems of resource allocation and routing, it is desired these two problems are addressed simultaneously. In this paper, we propose a joint resource allocation and routing scheme for an OFDMA-based multi-hop cellular system. This scheme uses a polynomial time heuristic algorithm called Multi-Dimensional Multi-choice Knapsack Problem (MMKP) in order to find an approximate solution maximizing the total downlink throughput. In the simulation results, we show that the proposed scheme finds a sub-optimal solution which is superior to a link quality-based routing scheme, but slightly worse than the optimal solution.

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Design of Lazy Classifier based on Fuzzy k-Nearest Neighbors and Reconstruction Error (퍼지 k-Nearest Neighbors 와 Reconstruction Error 기반 Lazy Classifier 설계)

  • Roh, Seok-Beom;Ahn, Tae-Chon
    • Journal of the Korean Institute of Intelligent Systems
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    • 제20권1호
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    • pp.101-108
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    • 2010
  • In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

Design of RBFNN-Based Pattern Classifier for the Classification of Precipitation/Non-Precipitation Cases (강수/비강수 사례 분류를 위한 RBFNN 기반 패턴분류기 설계)

  • Choi, Woo-Yong;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of the Korean Institute of Intelligent Systems
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    • 제24권6호
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    • pp.586-591
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    • 2014
  • In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.

Design of Echo Classifier Based on Neuro-Fuzzy Algorithm Using Meteorological Radar Data (기상레이더를 이용한 뉴로-퍼지 알고리즘 기반 에코 분류기 설계)

  • Oh, Sung-Kwun;Ko, Jun-Hyun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • 제63권5호
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    • pp.676-682
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    • 2014
  • In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

Plat-Based Key Pre-Distribution Scheme in Sensor Network (센서네트워크에서 평면 그리드 기반의 키 선 분배 기법)

  • Maeng, Young-Jae;Mohaisen, Abedelaziz;Lee, Kyung-Hee;Nyang, Dae-Hun
    • The KIPS Transactions:PartC
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    • 제15C권1호
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    • pp.1-8
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    • 2008
  • The security of wireless sensor networks is a challenging research area where the resources constraints are a bottleneck for any successful security design. Due to their computational feasibility, symmetric key algorithms that require key pre-distribution are more desirable for use in these networks. In the pre-distribution scheme, keys or keying materials are assigned to each node prior deployment to guarantee a secure communication within the entire network. Though several works are introduced on this issue, yet the connectivity and resiliency are imperfectly handled. In this paper, we revisit the grid based key pre-distribution scheme aiming to improve the connectivity, introduce a higher resiliency level, simplify the logic of key establishment and maintain same level of used of resources usage. The core of our modification relies on introducing the novel plat-based polynomial assignment and key establishment mechanism. To demonstrate the advantageous properties of our scheme over the revisited one, details of consumed resources, resulting connectivity, security and comparisons with relevant works are introduced.

Throughput Scaling Law of Hybrid Erasure Networks Based on Physical Model (물리적 모델 기반 혼합 소거 네트워크의 용량 스케일링 법칙)

  • Shin, Won-Yong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • 제18권1호
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    • pp.57-62
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    • 2014
  • The benefits of infrastructure support are shown by analyzing a throughput scaling law of an erasure network in which multiple relay stations (RSs) are regularly placed. Based on suitably modeling erasure probabilities under the assumed network, we show our achievable network throughput in the hybrid erasure network. More specifically, we use two types of physical models, a exponential decay model and a polynomial decay model. Then, we analyze our achievable throughput using two existing schemes including multi-hop transmissions with and without help of RSs. Our result indicates that for both physical models, the derived throughput scaling law depends on the number of nodes and the number of RSs.

Evolutionarily Optimized Design of Self-Organized Fuzzy Polynomial Neural Networks by Means of Dynamic Search Method of Genetic Algorithms (유전자 알고리즘의 동적 탐색 방법을 이용한 자기구성 퍼지 다항식 뉴럴 네트워크의 진화론적 최적화 설계)

  • Park Ho-Sung;Oh Sung-Kwun;Ahn Tae-Chon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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    • pp.475-478
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    • 2005
  • 본 논문에서는 자기구성 퍼지다항식 뉴럴 네트워크(SOFPNN)를 구성하고 있는 퍼지 다항식뉴론(FPM)의 구조와 파라미터를 유전자 알고리즘을 이용하여 최적화시킨 새로운 개념의 진화론적 최적 고급 자기구성 퍼지 다항식 뉴릴 네트워크를 소개한다. 기존의 자기구성 퍼지 다항식 뉴럴 네트워크에서 모델을 설계할 때에는 설계자의 주관적인 특징과 시행착오에 의해서 모델을 구축하였다. 이러한 설계자의 경험을 배제하고 객관적이고 효율적인 모델을 구축하기 위해서 본 논문에서는 FPH의 파라미터들을 최적화 알고리즘인 유전자 알고리즘을 이용하여 동조하였다. 즉, 모델을 구축하는데 기본이 되는 FPN의 각각의 파라미터들-입력변수의 수, 다항식 차수, 입력변수, 멤버쉽 함수의 수, 그리고 멤버쉽 함수의 정점-을 동조함으로써 기존의 모델에 비해서 구조적으로 그리고 파라미터적으로 최적화된 네트워크를 생성할 수 있다. 뿐만 아니라 주어진 데이터의 특성을 모델 구축에 반영하고자 멤버쉽 함수의 정점 역시 유전자 알고리즘으로 동조하였다. 실험적 예제를 통하여 제안된 모델의 성능을 확인한 결과 기존의 퍼지모델 및 신경망 모델에 비해서 아주 우수한 근사화 능력과 일반화 능력을 가짐을 알 수 있다.

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