Learning and extracting initial mealy automata with a modular neural network model

被引：0
|
作者：
机构：
来源：
Neural Comput | / 4卷 / 822期
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
下载
收藏
相关论文
共 50 条
  • [1] LEARNING AND EXTRACTING INITIAL MEALY AUTOMATA WITH A MODULAR NEURAL-NETWORK MODEL
    TINO, P
    SAJDA, J
    NEURAL COMPUTATION, 1995, 7 (04) : 822 - 844
  • [2] CGE: A Sequential Learning Algorithm for Mealy Automata
    Meinke, Karl
    GRAMMATICAL INFERENCE: THEORETICAL RESULTS AND APPLICATIONS, ICGI 2010, 2010, 6339 : 148 - 162
  • [3] The Application of the Method of the Convolution of Moore and Mealy Automata to Kahn Process Network Model
    Zvironiene, A.
    Zvironas, A.
    PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE VIBROENGINEERING 2008, 2008, : 11 - 14
  • [4] Extracting automata from neural networks using active learning
    Xu, Zhiwu
    Wen, Cheng
    Qin, Shengchao
    He, Mengda
    PEERJ COMPUTER SCIENCE, 2021, PeerJ Inc. (07) : 1 - 28
  • [5] A modular neural network architecture for inverse kinematics model learning
    Oyama, E
    Agah, A
    MacDorman, KF
    Maeda, T
    Tachi, S
    NEUROCOMPUTING, 2001, 38 : 797 - 805
  • [6] Neural network model for extracting optic flow
    Tohyama, K
    Fukushima, K
    NEURAL NETWORKS, 2005, 18 (5-6) : 549 - 556
  • [7] MODEL OF NEURAL NETWORK EXTRACTING BINOCULAR PARALLAX
    HIRAI, Y
    FUKUSHIMA, K
    BIOLOGICAL CYBERNETICS, 1975, 18 (01) : 19 - 29
  • [8] Extracting symmetry axes: A neural network model
    Fukusima, K
    Kikuchi, M
    2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2004, : 321 - 326
  • [9] Neural network model for extracting optic flow
    Fukushima, K
    Tohyama, K
    ARTIFICIAL NEURAL NETWORKS: BIOLOGICAL INSPIRATIONS - ICANN 2005, PT 1, PROCEEDINGS, 2005, 3696 : 455 - 460
  • [10] A Deep Learning Model for Extracting Consumer Sentiments using Recurrent Neural Network Techniques
    Ranjan, Roop
    Daniel, A. K.
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2021, 21 (08): : 238 - 246
12345