• Title, Summary, Keyword: Network structure

Search Result 4,540, Processing Time 0.058 seconds

Adaptive Structure of Modular Wavelet Neural Network (모듈화된 웨이블렛 신경망의 적응 구조)

  • 서재용;김용택;김성현;조현찬;전홍태
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • /
    • pp.247-250
    • /
    • 2001
  • In this paper, we propose an growing and pruning algorithm to design the adaptive structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angle criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. These criteria provide a methodology that a network designer can constructs wavelet neural network according to one's intention. The proposed growing algorithm grows the module and the size of modules. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristic of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the adaptive structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.

  • PDF

A STRUCTURAL ANALYSIS OF INTER-LIBRARY NETWORKS: A REGIONAL ILL NETWORK IN THE WESTERN NEW YORK 3Rs REGION (도서관 네트워크의 구조적 분석)

  • 유사라
    • Journal of the Korean Society for information Management
    • /
    • v.6 no.1
    • /
    • pp.37-56
    • /
    • 1989
  • This study is a structural analysis of a multi-type and multi-level library network within the framework of a regional interlibrary loan (ILL) system. The study monitored to information network structure for resource sharing of academic and research library materials transmitted through the ILL. The local flow of academic and research information was measured by a survey of the filled ILL transactions by individual libraries in the Western 3Rs region. The major findings were as follows: 1) the regional ILL network showed less than half of participation of the total subject libraries, 2) existing structure surveyed was identified as a composite centralized network with three communication groups, 3) depending on the types of materials transacted, the structure were changed, 4) statewide and multi-state library cooperatives had direct interactions with some of the local libraries, 5) individual libraries participated in the ILL network more for periodicals than book materials, 6) academic libraries throughout the total six structure analyzed showed the highest percentage of participation.

  • PDF

General Purpose Operation Unit Using Modular Hierarchical Structure of Expert Network (Expert Network의 모듈형 계층구조를 이용한 범용 연산회로 설계)

  • 양정모;홍광진;조현찬;서재용;전홍태
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • /
    • pp.122-125
    • /
    • 2003
  • By advent of NNC(Neural Network Chip), it is possible that process in parallel and discern the importance of signal with learning oneself by experience in external signal. So, the design of general purpose operation unit using VHDL(VHSIC Hardware Description Language) on the existing FPGA(Field Programmable Gate Array) can replaced EN(Expert Network) and learning algorithm. Also, neural network operation unit is possible various operation using learning of NN(Neural Network). This paper present general purpose operation unit using hierarchical structure of EN EN of presented structure learn from logical gate which constitute a operation unit, it relocated several layer The overall structure is hierarchical using a module, it has generality more than FPGA operation unit.

  • PDF

A Matrix-Based Genetic Algorithm for Structure Learning of Bayesian Networks

  • Ko, Song;Kim, Dae-Won;Kang, Bo-Yeong
    • International Journal of Fuzzy Logic and Intelligent Systems
    • /
    • v.11 no.3
    • /
    • pp.135-142
    • /
    • 2011
  • Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.

A Study on Optimal Neural Network Structure of Nonlinear System using Genetic Algorithm (유전 알고리즘을 이용한 비선형 시스템의 최적 신경 회로망 구조에 관한 연구)

  • Kim, Hong-Bok;Kim, Jeong-Keun;Kim, Min-Jung;Hwang, Seung-Wook
    • Journal of Navigation and Port Research
    • /
    • v.28 no.3
    • /
    • pp.221-225
    • /
    • 2004
  • This paper deals with a nonlinear system modelling using neural network and genetic algorithm Application q{ neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. in this paper, we optimize a neural network structure using genetic algorithm The genetic algorithm uses binary coding for neural network structure and searches for an optimal neural network structure of minimum error and fast response time. Through an extensive simulation, the optimal neural network structure is shown to be effective for identification of nonlinear system.

On-line Bayesian Learning based on Wireless Sensor Network (무선 센서 네트워크에 기반한 온라인 베이지안 학습)

  • Lee, Ho-Suk
    • Proceedings of the Korean Information Science Society Conference
    • /
    • /
    • pp.105-108
    • /
    • 2007
  • Bayesian learning network is employed for diverse applications. This paper discusses the Bayesian learning network algorithm structure which can be applied in the wireless sensor network environment for various online applications. First, this paper discusses Bayesian parameter learning, Bayesian DAG structure learning, characteristics of wireless sensor network, and data gathering in the wireless sensor network. Second, this paper discusses the important considerations about the online Bayesian learning network and the conceptual structure of the learning network algorithm.

  • PDF

The Network Characteristic Analysis of Research Projects on International Research Cooperation

  • Noh, Younghee;Kim, Taeyoun;Chang, Rosa
    • International Journal of Knowledge Content Development & Technology
    • /
    • v.8 no.4
    • /
    • pp.75-98
    • /
    • 2018
  • In this study, the network analysis of researchers, institutions, and research principal agent was conducted to understand structure characteristics of international cooperation research project implemented from 1997 to 2018. The network of researchers and institutions were decentralized structure. On the other hands, the network of research principal agent was centralized structure. The Soul National University is the leading organization of international cooperation research project. In terms of research principal agent, corporation is the leading principal agent. In additions, the results of the network centroid analysis of the researchers and institutions were correlated with the research funds. As a result, it was confirmed that the network centroid of research organization was linearly related to research funds.

Design of CNN with MLP Layer (MLP 층을 갖는 CNN의 설계)

  • Park, Jin-Hyun;Hwang, Kwang-Bok;Choi, Young-Kiu
    • Journal of the Korean Society of Mechanical Technology
    • /
    • v.20 no.6
    • /
    • pp.776-782
    • /
    • 2018
  • After CNN basic structure was introduced by LeCun in 1989, there has not been a major structure change except for more deep network until recently. The deep network enhances the expression power due to improve the abstraction ability of the network, and can learn complex problems by increasing non linearity. However, the learning of a deep network means that it has vanishing gradient or longer learning time. In this study, we proposes a CNN structure with MLP layer. The proposed CNNs are superior to the general CNN in their classification performance. It is confirmed that classification accuracy is high due to include MLP layer which improves non linearity by experiment. In order to increase the performance without making a deep network, it is confirmed that the performance is improved by increasing the non linearity of the network.

The Impact of Network Structure on Legislative Performance in Cosponsorship Networks (공동발의 네트워크에서 국회의원의 네트워크 구조가 입법 성과에 미치는 영향)

  • Seo, Il-Jung
    • The Journal of the Korea Contents Association
    • /
    • v.18 no.9
    • /
    • pp.433-440
    • /
    • 2018
  • I investigated whether network structure of legislators affects legislative performance in a cosponsorship network. I presented the theoretical basis with network closure and structural holes, and analyzed the network in the 19th National Assembly of Republic of Korea. In the directed and weighted network, each tie means that cosponsors support the bill sponsors proposed. The performance was measured by the number of initiatives and the ratio of reflected legislation, and the network structure was measured by size, density, hierarchy, and constraint. I found that the legislators with brokerage structure have a lot of initiatives in making connection with many legislators in various groups and the legislators with hierarchical structure have the higher ratio of reflected legislation with the continuous and strong support from the members of their group. I also found that the network of ruling party lawmaker is more hierarchical than the network of opposition lawmaker.

A Study on the Bayesian Recurrent Neural Network for Time Series Prediction (시계열 자료의 예측을 위한 베이지안 순환 신경망에 관한 연구)

  • Hong Chan-Young;Park Jung-Hoon;Yoon Tae-Sung;Park Jin-Bae
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.10 no.12
    • /
    • pp.1295-1304
    • /
    • 2004
  • In this paper, the Bayesian recurrent neural network is proposed to predict time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one needs to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, the weights vector is set as a state vector of state space method, and its probability distributions are estimated in accordance with the particle filtering process. This approach makes it possible to obtain more exact estimation of the weights. In the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent neural network with Bayesian inference, what we call Bayesian recurrent neural network (BRNN), is expected to show higher performance than the normal neural network. To verify the proposed method, the time series data are numerically generated and various kinds of neural network predictor are applied on it in order to be compared. As a result, feedback structure and Bayesian learning are better than feedforward structure and backpropagation learning, respectively. Consequently, it is verified that the Bayesian reccurent neural network shows better a prediction result than the common Bayesian neural network.