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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉長遠 | |
| dc.contributor.author | Ching-Teng Ling | en |
| dc.contributor.author | 凌璟騰 | zh_TW |
| dc.date.accessioned | 2021-06-16T13:39:34Z | - |
| dc.date.available | 2017-07-26 | |
| dc.date.copyright | 2013-07-26 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-07-15 | |
| dc.identifier.citation | [1] M. A. Arbib. Simple self-reproducing universal automata. Information and Control,
pages 177–189, 1966. [2] E. R. Banks. Information processing and transmission in cellular automata. 1971. [3] B. M. Blumberg, P. M. Todd, and P. Maes. No bad dogs: Ethological lessons for learning in hamsterdam. In From Animals to Animats, Proceedings of the Fourth International Conference on the Simulation of Adaptive Behavior, MIT Press/Bradford Books, Cambridge, MA, pages 295–304, 1996. [4] P. P. Chaudhuri, D. R. Chowdhury, S. Nandi, and S. Chattopadhyay. Additive cellular automata: theory and applications, volume 1. Wiley-IEEE Computer Society Press, 1997. [5] W.-C. Cheng, J.-C. Huang, and C.-Y. Liou. Segmentation of dna using simple recurrent neural network. Knowledge-Based Systems, 26:271–280, 2012. [6] L. O. Chua. A nonlinear dynamics perspective of Wolfram’s new kind of science. World Scientific, 2006. [7] E. F. Codd. Cellular Automata. Academic Press, Inc., Orlando, FL, USA, 1968. [8] A. Das, A. Ganguly, A. Dasgupta, S. Bhawmik, and P. Chaudhuri. Efficient characterisation of cellular automata. Computers and Digital Techniques, IEE Proceedings E, 137(1):81–87, 1990. [9] E. Fredkin. An informational process based on reversible universal cellular automata. Physica D: Nonlinear Phenomena, 45(1):254–270, 1990. [10] K. Fu. 11 stochastic automata as models of learning systems. In J. Mendel and K. Fu, editors, Adaptive, Learning and Pattern Recognition Systems Theory and Applications, volume 66 of Mathematics in Science and Engineering, pages 393 – 431. Elsevier, 1970. [11] M. Gardner. Mathematical games: The fantastic combinations of john conway’s new solitaire game ”life”. Scientific American, 223(4):120–123, 1970. [12] M. Gardner. Mathematical games: The fantastic combinations of john conway’s new solitaire game ”life”. Scientific American, 223(4):120–123, 1970. [13] M. Gerhardt and H. Schuster. A cellular automaton model describing the formation of excitable media including curvature and dispersion. Physica D: Nonlinear Phenomena, 36(3):209–221, 1989. [14] T. G. Ksiazek, D. Erdman, C. S. Goldsmith, S. R. Zaki, T. Peret, S. Emery, S. Tong, C. Urbani, J. A. Comer, W. Lim, et al. A novel coronavirus associated with severe acute respiratory syndrome. New England Journal of Medicine, 348(20):1953–1966, 2003. [15] Y. C. Lee, S. Qian, R. D. Jones, C. W. Barnes, G. W. Flake, M. K. O’Rourke, K. Lee, H. H. Chen, G. Z. Sun, Y. Q. Zhang, D. Chen, and C. L. Giles. Adaptive stochastic cellular automata: Theory. Physica D Nonlinear Phenomena, 45:159–180, Sept. 1990. [16] C.-Y. Liou, C.-H. Tan, H.-T. Chen, and J.-H. Chen. Agents that have desires and adaptive behaviors. In S. Usui and T. Omori, editors, ICONIP, pages 845–849, 1998. [17] O. Martin, A. M. Odlyzko, and S. Wolfram. Algebraic properties of cellular automata. Communications in mathematical physics, 93(2):219–258, 1984. [18] S. R. Michalski, G. J. Carbonell, and M. T. Mitchell, editors. Machine learning an artificial intelligence approach volume I,II. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1986. [19] S. Qian, Y. Lee, R. Jones, C. Barnes, G. Flake, M. O’Rourke, K. Lee, H. Chen, G. Sun, Y. Zhang, et al. Adaptive stochastic cellular automata: applications. Physica D: Nonlinear Phenomena, 45(1):181–188, 1990. [20] P. Sarkar and R. Barua. The set of reversible 90150 cellular automata is regular. Discrete Applied Mathematics, 84:199 – 213, 1998. [21] A. R. Smith III. Introduction to and survey of polyautomata theory. Automata, languages, development, pages 405–422, 1976. [22] R. S. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. Advances in neural information processing systems, pages 1038–1044, 1996. [23] J. Von Neumann. The general and logical theory of automata. Cerebral mechanisms in behavior, pages 1–41, 1951. [24] J. von Neumann. Theory of self-reproducing automata. University of Illinois Press, Champaign, IL, 1966. [25] R. T. Wainwright. Life is universal! In Proceedings of the 7th conference on Winter simulation-Volume 2, pages 449–459. Winter Simulation Conference, 1974. [26] S. Wolfram. Statistical mechanics of cellular automata. Reviews of modern physics, 55(3):601, 1983. [27] S. Wolfram. A New Kind of Science. Wolfram Media, 2002. [28] A. Wuensche and M. Lesser. The global dynamics of cellular automata: An atlas of basin of attraction fields of one-dimensional cellular automata, volume 1. Andrew Wuensche, 1992. [29] T. Yada, Y. Totoki, M. Ishikawa, K. Asai, and K. Nakai. Automatic extraction of motifs represented in the hidden Markov model from a number of DNA sequences. Bioinformatics, 14(4):317–25, 1998. [30] K. Zuse. Rechnender raum. Physik und Informatik-Informatik und Physik, Arbeitsgespr ‥ ach, pages 16–23, 1991. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62299 | - |
| dc.description.abstract | 在這篇論文中，我們設計了一個可以預測一個有規則的序列中的下
一個符號的感測器，並且可以透過學習算出在符號序列中的規則。我 們使用的強化學習法來設計學習過程，並使用隨機格狀自動機來實作 強化學習法。這個感測器可以應用在許多問題上，例如：預測基因序 列、股票市場、偵測傳送錯誤或是網路攻擊。為了要展示我們的感測 器，在這篇論文中，我們設計一個感測器可以用來預測基因序列，並 分析結果。 | zh_TW |
| dc.description.abstract | In this paper, we present a ruled symbol sequence sensor which can predict the next symbol of a symbol sequence and extract the rules of the symbol
sequence. In this sensor, we use the reinforcement learning mechanism to design the learning process, and use the stochastic cellular automata to implement the value function in the reinforcement learning model. This sensor can be applied on many problems, such as prediction of DNA sequence, stock market, transaction anomalies, internet Intrusion and transmission anomaly. For demonstrating our sensor, we apply the sensor on some DNA sequences and analyze the output. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T13:39:34Z (GMT). No. of bitstreams: 1 ntu-102-R99922090-1.pdf: 1746943 bytes, checksum: 17acb45fd7d1f4383daa873bbe377591 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii 1 Introduction 1 2 Reinforcement learning 3 2.1 Reinforcement learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Markov Decision Process . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 performance measurement(value function) . . . . . . . . . . . . . . . . . 4 3 Cellular Automata 6 3.1 History of Cellular automata . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Types of Cellular automata . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3 Characterization of Cellular automata . . . . . . . . . . . . . . . . . . . 8 3.4 Adaptive stochastic cellular automata . . . . . . . . . . . . . . . . . . . 10 4 Formula for Cellular automata and Stochastic cellular automata 11 4.1 Stochastic cellular automata . . . . . . . . . . . . . . . . . . . . . . . . 12 4.2 Reinforcement learning models . . . . . . . . . . . . . . . . . . . . . . . 13 5 Method 15 5.1 define variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5.2 learning algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 6 Experiment 17 6.1 SARS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 6.2 analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 6.3 Adaptive stochastic cellular automata . . . . . . . . . . . . . . . . . . . 26 6.4 The types of grouping output . . . . . . . . . . . . . . . . . . . . . . . . 31 7 Conclusion 32 Bibliography 33 | |
| dc.language.iso | en | |
| dc.subject | 強化學習法 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 預測符號序列 | zh_TW |
| dc.subject | DNA分析 | zh_TW |
| dc.subject | DNA analyze | en |
| dc.subject | reinforcement learning | en |
| dc.subject | prediction of symbol sequence | en |
| dc.subject | machine learning | en |
| dc.title | 透過隨機細胞自動機學習DNA序列規則 | zh_TW |
| dc.title | Find Rules From DNA Sequences By Stochastic Cellular
Automata | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 呂育道,鄭為正,黃昭綺 | |
| dc.subject.keyword | 機器學習,強化學習法,預測符號序列,DNA分析, | zh_TW |
| dc.subject.keyword | machine learning,reinforcement learning,prediction of symbol sequence,DNA analyze, | en |
| dc.relation.page | 35 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-07-15 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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