(54.145.95.149) 您好!臺灣時間:2017/08/23 20:10          離開系統
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
本論文永久網址: 
研究生:林國銘
研究生(外文):Kuo-Ming Lin
論文名稱:應用四足筷子機器人於探討適應性學習之問題
論文名稱(外文):On the Study of Adaptive Learning Problems with a Quadruped Chopstick Robot
指導教授:陳重臣陳重臣引用關係
指導教授(外文):Jong-Chen Chen
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:77
中文關鍵詞:類分子神經系統演化式機器人筷子機器人
外文關鍵詞:Artificial Neuron Molecular SystemChopstick RobotEvolvable Robot
相關次數:
  • 被引用被引用:0
  • 點閱點閱:314
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:52
  • 收藏至我的研究室書目清單書目收藏:0
仿生物晶片的出現讓機器人更增加了彈性與適應性,使得機器人不管是二足、四足或多足於行走上,都能夠走得更穩建與擁有適應性,然而,欲克服機器人之適應性學習問題,往往需付出相當高昂之代價。本研究以類分子神經系統(Artificial NeuroMolecular System , ANM)為架構[Chan, 1993],製作出一隻名為小可(Miky)之四足筷子機器人,並利用無線滑鼠作為行走動作之回饋式感測器,ANM的運作方式相當接近人類大腦,其具有可演化及快速學習之特性,能快速傳遞控制信號至控制晶片,使Miky能夠自我學習與判斷選擇行走之模式。本研究將此系統稱為ANM-R,實驗證明ANM-R能夠解決機器人對於適應性學習之問題,達到快速學習之能力,且能讓輕型行走機器人以更穩建及適當之方式行走於平地或崎嶇有障礙之道路上,也兼具克服機器人製作費用高昂之問題。
The appearance attempt of biochip lets the robotic hardware also increases flexibility and adaptability to make robots whether two feet, four feet or several feet on their walking both steady and adaptable. However, the robots always have been expensive in solving adaptive problems. This research made a quadruped chopstick robot controlled by a Artificial Neuron Molecular System (ANM) [Chan, 1993], named Miky, has been developed. Miky used an optomechanical mouse to be a sensor of walking feedback, ANM has operation method of electric circuit nears a human brain to make Miky has the characteristic can be evolved and learned by itself. And, ANM fast delivering signals to chip of controller makes Miky learned and chose walking modes by itself, too. This research called the system is ANM-R. ANM reach to ability of fast learning during the period of testing, it made this walking robot can walk steady and fit on the different roads too. And, the walking robot is cheap in manufacture.
中文摘要 i
英文摘要 ii
誌 謝 iii
目 錄 iv
表 目 錄 v
圖 目 錄 vi
一、緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究限制 4
1.4 論文架構 4
二、文獻探討 6
2.1 類分子神經系統(ANM) 6
2.2 內部動態或神經元內部資訊處理 12
2.3 適應性理論 13
2.4 演化式學習 15
2.5 應用演化式學習於機器人 16
三、研究方法 17
3.1 系統架構 19
3.2 細胞骨架模式 20
3.3 ANM進化式學習 24
3.4 輸入/輸出界面 29
3.5 ANM系統結構與功能關係說明 30
3.6 實驗設備 32
四、實驗結果與討論 35
4.1持續學習能力實驗結果 36
4.1.1模仿動物-六個行走的模式 37
4.1.2初始訓練 43
4.2 適應性能力實驗結果 47
4.2.1 適應力分析 48
4.2.2 容錯能力分析 60
4.3 實驗總結 64
五、結論 66
5.1 研究結論 66
5.2 未來展望 67
5.3 未來研究方向 68
參考文獻 70
[1]林昇甫、洪成安,1992,神經網路入門與圖樣辦識,全華科技出版。
[2]林侑賢,2003,可演化學習的多層類分子神經系統之數位電路建構,雲林科技大學,碩士論文。
[3]陳柏儒,2003,撞球機器人之類神經模糊補償器設計,淡江大學,碩士論文。
[4]陳重德,2003,以模仿的方法促使機器人學習行為的模式,屏東科技大學,碩士論文。
[5]張詠翔,2006,應用細胞自動機於可演化多層類分子神經系統之數位電路建構,雲林科技大學,碩士論文。
[6]蘇明祥,2003,使用者導向之情感寵物機器人設計系統,屏東科技大學,碩士論文。
[7]葉怡成,1994,類神經網路-模式應用與實作,儒林圖書,台北市。
[8]葉怡成,1998,應用類神經網路,儒林書局,台北市。
[9]廖國勛,2002,以能量傳遞及整合實作多層類分子神經系統及其應用,雲林科技大學,碩士論文。
[10]吳守哲,2008,結合細胞自動機與類分子神經系統之演化式數位電路,雲林科技大學,碩士論文。
[11]Andres Upegui, (2005),Carlos Andre´s Pen˜a-Reyes, Eduardo Sanchez, An FPGA platform for on-line topology exploration of spiking neural networks, Microprocessors and Microsystems 29 211–223
[12]Anderson, J.A. Cognitive and psychological computation with neural models. IEEE Transactions on Systems, Man, and Cybernetics SMC-13, 5, (1983), 799-815.
[13]Barto, A.G., Sutton, R.S., and Brewer, P.S. Synthesis of nonlinear control surfaces by a layered associative search network. Biol. Cybernetics, 43, (1981), 175-185.
[14]Bremermann, H.J. Optimization through evolution and recombination, in Self-Organizing Systems, Yovits, Jacobi and Goldstein, eds. (Spartan Books, Washington, D.C., 1962), 93-106.
[15]Chen, J.-C., (1993),“Computer experiment on evolutionary learning in a multi level neuromolecular architecture,” Ph.D. Dissertation, Department of Computer Science, Wayne State University, Detroit, U.S.A.
[16]Chen, J.-C. and M. Conrad, (1994a), “Learning Synergy in a Multilevel Neuronal Architecture,” BioSystem, Vol.32 pp. 111-142.
[17]Chen, J.-C. and M. Conrad, (1994b), “A Multilevel Neuromolecular Bypass Principle to Facilitate Evolutionary Learning,” Physica D, Vol.75 pp. 417-437.
[18]Chen, J.-C., (1998), “Problem Solving with a Perpetual Evolutionary Learning Architecture,” Applied Intelligence, , pp. 53-71.
[19]Chen, J.-C., (2001), “Toward an Evolvable Neuromolecular Hardware: Realization of A Multilevel Brain-Like Architecture with Digital Circuits,” Neuron Computing.(accepted).
[20]Carter, F.L., Siatkowski, R.E., and Wohltjen, Molecular Electronic Devices (1988) (North-Holland, Amsterdam).
[21]Chen, J.C., 1993, Computer experiments on evolutionary learning in a multilevel neuromolecular architecture, Ph.D. Dissertation, Department of Computer Science, Wayne State University, Detroit, U.S.A.
[22]Chen, J.C., 1995a, A synergetic multilevel artificial brain for pattern recognition/neurocontrol. Proceedings of the Fifth International Symposium Bioelectronic and Molecular Electronic Devices and the Sixth International Conference on Molecular Electronics and BioComputing, Okinawa, Japan, 247-250.
[23]Chen, J.C, 1995b, Pattern recognition with multilevel learning algorithms, Proceedings of the 20th Annual Chinese-American Academic Professional Society Convention, New York, U.S.A., 88.
[24]Chen, J.C., 1996a, Simulation of a discrete-event brain-like computer model in pattern recognition, Proceedings of 1996 Summer Computer Simulation Conference, eds. V.W. Ingalls, J. Cynamon, A.V. Saylor, SCS, San Diego, U.S.A., 193-198.
[25]Chen, J.C., 1996b, Pattern recognition using an adaptive evolutionary learning architecture, Proceedings of the Adaptive, Distributed, and Parallel Computing Symposium, Dayton, Ohio., U.S.A., 228-238.
[26]Chen, J.C., 1996c, An artificial neuromolecular computer system that facilitates self-organizing learning and its application to Chinese character recognition, Proceedings of the 13th Workshop on Combinatorial Mathematics and Computation Theory, Taichung, Taiwan, 167-174.
[27]Chen, J.C., 1996d, A coevolving multilevel learning system for pattern categorization and generalization, Proceedings of the First Asia-Pacific Conference on Simulated Evolution and Learning (in conjunction with Micro-Robot World Cup Soccer Tournament MIROSOT‘96), Taejon, South Korea, 135-142.
[28]Chen, J.C., 1996e, Adaptive pattern recognition with a self-organizing learning architecture, Proceedings of 1996 International Computer Symposium, Kaohsiung, Taiwan, 151-158.
[29]Chen, J.C., 1997a, Multiscale syngery in an artificial neuromolecular architecture, To appear in Proceedings of the International Workshop on Central Auditory Processing and Neural Modeling, Taiwan.
[30]Chen, J.C., 1997b, A biologically motivated multilevel learning architecture for network packet routing problems, To appear in Proceedings of the Australia-Pacific Forum on Intelligent Processing and Manufacturing of Materials, Gold Coast, Queensland, Australia, 1997.
[31]Chen, J.C. and Conrad, M., 1993, Problem solving using a multilevel neuronal architecture, Proceedings of the Second Annual Evolutionary Programming, San Diego, CA., U.S.A., 310-319.
[32]Chen, J.C. and Conrad, M., 1994a, Learning synergy in a multilevel neuronal architecture, 32, BioSystems, 111-142.
[33]Chen, J.C. and Conrad, M., 1994b, A multilevel neuromolecular architecture that uses the extradimensional bypass principle to facilitate evolutionary learning, 75, Physica D., 417-437.
[34]Chen, J.C. and Conrad, M., 1996a, Pattern categorization and generalization with a virtual neuromolecular architecture, Neural Networks (in press).
[35]Chen, J.C. and Conrad, J.C., 1997, Evolutionary learning with a neuromolecular architecture: Motivations for the biological approach to computational adaptability, Soft Computing Research Journal (in press).
[36]Chen, J.C. and Hura, G.S., 1989, Design solution for load balancing problem in distributed systems using generalized time petri nets, Proceedings of the Twentieth Annual Pittsburgh Conference on Modeling & Simulation, Pittsburgh, U.S.A., 1111-1115.
[37]Chen, S.H., 1996, Would and Should Government Lie about Economic Statistics: Simulations Based on Evolutionary Cellular automata, Proceeding of the First Asia-Pacific Conference on Simulated Evolution and Learning, Taejon, South Korea, 365-372.
[38]Conrad, M. Information processing in molecular systems. Currents in Modern Biology (now BioSystems) 5 (1972), 1-14.
[39]Conrad, M. Molecular information structures in the brain. J. Neurosci. Res. 2 (1976a), 233-254.
[40]Conrad, M. Complementary molecular models of learning and memory. BioSystems 8 (1976b), 119-138.
[41]Conrad, M. Principle of superposition-free memory. J. Theor. Biol. 67 (1977a), 213-219.
[42]Conrad, M. Adaptability (Plenum, New York, 1983).
[43]Conrad, M. Electronic instabilities in biological information processing, in Molecular Electronics, P.I. Lazarev, ed. (Kluwer Academic Publishers, Amsterdam, 1991), 41-50.
[44]Conrad, M., Kampfner, R.R., and Kirby, K.G. Simulation of a reaction-diffusion neuron which learns to recognize events, Appendix to Conrad, M. Reapprochement of artificial intelligence and dynamics. Eur. J. Operational Res. 30, (1987), 280-290.
[45]Conrad, M., Kampfner, R.R., and Kirby, K.G. Neuronal dynamics and evolutionary learning, in Advances in Cognitive Science, M. Kochen and H.M. Hastings, eds. (Westview Press, Boulder, Colorado, 1988), 169-189.
[46]Conrad, M., Kampfner, R.R., Kirby, K.G., Rizki, E.N., Schleis, G., Smalz, R. and Trenary, R. Towards an artificial brain. BioSystems 23 (1989), 175-218.
[47]Dianxun Shuaia, Yumin Dongc, and Qing Shuai, (2007), A new data clustering approach : Generalized cellular automata , Information Systems 32 968–977
[48]E.A. Liberman, S.V. Minina, and K.V. Golubtsov, (1975), “The study of the metabolic synapse II: comparison of cyclic 3''5''-AMP and cyclic 3''5''-GMP effects,” Biophysics 22 pp. 75-81.
[49]E.A. Liberman, S.V. Minina, N.E. Shklovsky-Kordy, and M. Conrad, (1982), “Microinjection of cyclic nucleotides provides evidence for a diffusional mechanism of intraneuronal control,” BioSystems 15 pp. 127-132.
[50]Ebeling, G.M. and Freistal, R. Physik der Selbstorganisation und Evolution, (1982), (Akademie Verlag, Berlin).
[51]Edelman, G.M. Group selection and phasic reentrant signaling - a theory of higher brain function, in The Mindful Brain, G.M. Edelman, and V.B. Mountcastle, eds. (MIT Press, Cambridge, Mass., 1978), 51-100.
[52]Edelman, G.,M., Neural Darwinism: The Theory of Neuronal Group Selection (Basic Books, Inc., New York, 1987).
[53]Fogel, L., Owens, A., and Walsh, M. Artificial Intelligence Through Simulated Evolution (Wiley, New York, 1966).
[54]Gardner, M.R. and Ashby, W.R. Conductance of large dynamical (cybernetic) systems: critical values for stability. Nature, 228, (1970), 784.
[55]Goldberg, D.E., Genetic Algorithms in Search , Optimization and Machine Learning , (1989), (Addison-Wesley, Reading, Mass.).
[56]Grossberg, S. How does a brain build a cognitive code. Psychological Review (1980), 1-51.
[57]G. Matsumoto, S. Tsukita, and T. Arai, (1989), “Organization of the axonal cytoskeleton: differentiation of the microtubule and actin filament arrays,” in: F.D. Warner and J.R. McIntosh, eds., Kinesin, Dynein, Cell Movement, Microtubule Dynamics(Alan R. Liss, New York) pp. 335-356.
[58]Gunnar Tufte, (1999), “Prototyping a GA Pipeline for Complete Hardware Evolution,” EH''99, pp. 143-150.
[59]Hameroff, S.R. and Watt, R.C., Information processing in microtubules. J. Theor. Biol., 98, (1982), 549-561.
[60]Hameroff, S.R. Ultimate Computing (North-Holland, Amsterdam, 1987).
[61]Hameroff, S.R., Rasmussen, S. and Mansson, B. Molecular automata in microtubules: basic computational logic for the living state?, in Artificial Life, C. Langton, ed. (Addison-Wesley, Reading, MA, 1989), 521-553.
[62]Hameroff, S.R., Dayhoff, J.E., Lahoz-Beltra, R., Samsonovich, A. and Rasmussen, S. Conformational automata in the cytoskeleton: models for molecular computation. Computer 25, No. 11(1992), 30-39.
[63]Hastings, H.M. The May-Wigner stability theorem. J. Theor. Biol. 97 (1982), 155-166.
[64]Hebb, D.O. The first stage of perception: growth of the assembly, The Organization of Behavior (1949), 60-78.
[65]Holland, J. Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, MI., 1975).
[66]Holland, J. A mathematical framework for studying learning in classifier systems. Physica 22D, (1986), 307-317.
[67]Hopfield, J. Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79 (1982), 2554-2558.
[68]I. Kajitani, M. Murakawa, D. Nishikawa, H. Yokoi, N. Kajihara, M. Iwata, D. Keymeulen, H. Sakanashi and T. Higuchi Proc, (1999), “An Evolvable Hardware Chip for Prosthetic Hand Controller,” Bio-Inspired Systems (MicroNeuro99) pp. 179-186.
[69]Jaechul Sung a, Deukjo Hong b, and Seokhie Hong, (2007) , Cryptanalysis of an involutional block cipher using cellular automata, Information Processing Letters 104 183–185
[70]Jason C. Isaacs, Robert K. Watkins, and Simon Y. Foo, (2003), Cellular automata PRNG : maximal performance and minimal space FPGA implementations, Engineering Applications of Artificial Intelligence 16 491–499
[71]Kampfner, R.R. and Conrad, M. Computational modeling of evolutionary learning processes in the brain. Bull. Math. Biol. 45 (1983), 931-968.
[72]Kirby, K.G. and Conrad, M. The enzymatic neuron as a reaction-diffusion network of cyclic nucleotides. Bull. Math. Biol. 46 (1984), 765-782.
[73]Kirby, K.G. and Conrad, M. Intraneuronal dynamics as a substrate for evolutionary learning. Physica D 22 (1986), 205-215.
[74]Kirkpatrick, F.H. New models of cellular control: membrane cytoskeletons, membrane curvature potential, and possible interactions. BioSystems 11 (1979), 85-92.
[75]Kirley, M.G., 1996, Applications of Genetic Algorithms to Solving Scheduling Problems, Proceedings of the First Asia-Pacific Conference on Simulated Evolution and Learning, Taejon, South Korea, 419-426.
[76]Koruga, D., Molecular networks as a sub-neural factor of neural networks. BioSystems, 23, (1990), 297-303.
[77]Lee, C.H. and Kim, J.K., The Identification of Nonlinear Systems using EONNs, Proceedings of the First Asia-Pacific Conference on Simulated Evolution and Learning, Taejon, South Korea, 343-353.
[78]Liberman, E.A., Minina, S.V., Golubtsov, K.V. The study of the metabolic synapse II: comparison of cyclic 3'',5''-AMP and cyclic 3'',5''-GMP effects. Biophysics 22 (1975), 75-81.
[79]Liberman, E.A., Minina, S.V., Shklovsky-Kordy, N.E., and Conrad, M. Microinjection of cyclic nucleotides provides evidence for a diffusional mechanism of intraneuronal control. BioSystems 15 (1982a), 127-132. Liberman, E.A., Minina, S.V., Shklovsky-Kordy, N.E., and Conrad, M. Change of mechanical parameters as a possible means for information processing by the neuron (in Russian). Biophysics 27 (1982b), 863-870.
[80]Liberman, E.A., Minina, S.V., Mjakotina, O.L., Shklovsky-Kordy, N.E., and Conrad, M. Neuron generator potentials evoked by intracellular injection of cyclic nucleotides and mechanical distension. Brain Res. 338 (1985), 33-44.
[81]Matsumoto, G., Tsukita, S., and Arai, T. Organization of the axonal cytoskeleton: differentiation of the microtubule and actin filament arrays, in Cell Movement, Vol. 2: Kinesin, Dynein, and Microtubule Dynamics, F.D. Warner and J.R. McIntosh, eds. (Alan R. Liss, New York, 1989), 335-356.
[82]Matus, a. and Riederer, B., Microtubule-associated proteins in the developing brain. Ann. NY Acad. Sci., 466, (1986), 167-179.
[83]May, R.M. Stability and Complexity in Model Ecosystems (Princeton University Press, Princeton, New Jersey, 1973).
[84]Maynard-Smith, J. Natural selection and the concept of a protein space. Nature, 225, (1970), 563-564.
[85]McCulloch, W.S. and Pitts, W. A logical calculus of the ideas imminent in nervous activity. Bull. Math. Biophysics 5 (1943), 115-133.
[86]Minsky, M. Steps toward artificial intelligence. Proceedings of the Institute of Radio Engineers, (1961), 49.
[87]Minsky, M. and Papert, S. Perceptrons: An Introduction to Computational Geometry, (1972), (Cambridge, MIT Press).
[88]Minsky, M., K-lines: a theory of memory. Cognitive Science 4 (1980), 117-133.
[89]M. Conrad, (1974), “Evolutionary learning circuits,” J. Theor. Biol. 46, 1974, pp. 67-188.
[90]M. Conrad, (1976a), “Molecular information structures in the brain”, J. Neurosci. Res. 2pp. 233-254.
[91]M. Conrad, (1976b), “Complementary molecular models of learning and memory,” BioSystems 8 pp. 119-138.
[92]M. Conrad, R.R. Kampfner, and K.G. Kirby, (1988), “Neuronal dynamics and evolutionary learning,” in: M. Kochen and H. Hastings, eds., Advances in Cognitive Science: Steps Toward Convergence 104 (Westview Press, Boulder, CO,) pp. 169-189.
[93]M. Conrad, (1989), “The brain-machine disanalogy,” BioSystems 22 pp. 197-213.
[94]M. Conrad, (1991), “Electronic instabilities in biological information processing,” in: P.I. Lazarev, ed., Molecular Electronics (Kluwer Academic Publishers, Amsterdam,) pp. 41-50.
[95]M. Salami, H. Sakanashi, M. Tanaka, M. Iwata, and T. Higuchi, (1998), “On-Line Compression of High Precision Printer Images by Evolvable Hardware,” DCC ''98 Data Compression Conference, IEEE Computer Society Press219-228.
[96]Nicholas J. Macias, (1999), “Ring Around the PIG: A Parallel GA with Only Local Interactions Coupled with a Self-Reconfigurable Hardware Platform to Implement an O(1) Evolutionary Cycle for Evolvable Hardware,” IEEE, Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1067~1075.
[97]Nerome, M., Yamada, K., Endo, S., and Miyagi, H., 1996, Competitive Co-evolution Model on the Acquisition of Game Strategy, Proceedings of the First Asia-Pacific Conference on Simulated Evolution and Learning, Taejon, South Korea, 357-364.
[98]Nicholis, G. and Prigogine, I., Self-Organization in Nonequilibrium Systems, (1977), (Wiley Interscience, New York).
[99]Park, S.H., Park, K.C. , and P.H., Chang, 1996, Global Path Planning of Robot Manipulator with Kinematic Redundancy and Its Applications, Proceedings of the First Asia-Pacific Conference on Simulated Evolution and Learning, Taejon, 227-234.
[100]Przemyslaw Wrzos and Andrew Price Autonomous Aircraft Rearch Group, “UAV Flight Controller on an FPGA,” ICARA 2006, pp.575-580.
[101]Rechenberg, I., Evolutionary Stragegie: Optimierung Technischer Systeme nach Prinzipier der Biologischen Evolution (1973), (Frommann-Holzboog, Stuggart).
[102]Reeke, G.N. and Eleman, G.M. Selective networks and recognition automata, in Advance in Cognitive Science, M. Kochen and H.M. Hastings, eds, (1988), (AAAS. Washington, D.C.). Wiley
[103]Rizki, M. and Chen, J.C., 1993, Mutation and recombination effects on the adaptability of sexual and asexual organism, Proceedings of the First Great Lakes Computer Science Conference, Kalamazoo, MI., U.S.A., 399-405.
[104]Rosen, R. Dynamical System Theory in Biology (1970), (John and Sons, New York).
[105]Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65 (1958), 386-408.
[106]Rumelhart, D.E., Hinton, G.E., and Williams, R.J., Learning internal representation by error propagation, in: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, D.E. Rumelhart and J. L. McClelland, eds, (1986), (MIT Press, Cambridge, MA).
[107]Samsonovich, A., Scott, a., and Hameroff, S. Acoustic-conformational transitions in cytoskeletal microtubules: implications for intracellular information processing. Nanobiology 1, (1992), 457-468.
[108]Schwefel, H.P. Numerical Optimization of Computer Models (1981), (Wiley, Chichester).
[109]Sejnowski, T.J., Kienker, P.K., and Hinton, G.E., Learning symmetry groups with hidden units: beyond the percetpron. Physica 22D, (1986), 260-275.
[110]Selden, S.C. and Pollard, T.D. Phosphorylation of microtubule-associated proteins regulates their interaction with actin filaments. J. Biol. Chem. 258 (1983), 7064-7071.
[111]Spiessens, P. and Torreele, J., Massively parallel evolution of recurrent networks: an approach to temporal processing, in : Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, F.J. Varela and P. Bourgnine, eds., (1992), 70-77.
[112]Steinert, P.M., Jones, J.C.R., and Goldman, R.D., Intermediate filaments. J. Cell Biol. 99 (1984), 22s-27s.
[113]S.R. Hameroff, J.E. Dayhoff, R. Lahoz-Beltra, A. Samsonovich, and S. Rasmussen, (1992),“Conformational automata in the cytoskeleton: models for molecular computation,” Computer 25, 11 pp. 30-39.
[114]T. Higuchi, M. Murakawa, M. Iwata, I. Kajitani, W. Liu, and M. Salami(1992), “Evolvable Hardware at Function Level,” Proc. 1997 IEEE Int. Conf. on Evolutionary Computation (ICEC97), 1997, pp. 187-192. P. Werbos, “The Cytoskeleton: Why It May Be Crucial to Human Learning and to Neurocontrol,” Nanobiology, vol. 1,, pp. 75-95.
[115]Vallee, R.B., Bloom, G.S., and Theurkauf,W.E. Microtubule-associated proteins: subunits of the cytomatrix. J. Cell Biol. 99 (1984), 38s-44s.
[116]Werbos, P. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Ph.D. Thesis, Harvard University (1974).
[117]Werbos, P. Backpropagation and neurocontrol: a review and prospectus, in Proceedings of the International Joint Conference on Neural Networks (1989).
[118]Werbos, P., The cytoskeletal: why it may be crucial to human learning and neurocontrol. Nanobiology, 1, (1992), 75-96.
[119]Whitley, D. and Hanson, T., Optimizing neural networks using faster, more accurate genetic search, Proceedings of the 3rd Intern. conference on Neural Networks, IEEE, (1989), 157-255.
[120]Wolfram, S., 1983, Statistical mechanics of cellular automata. Rev. Mod. Physics, 55, (1983), 601-644.
[121]Wolfram, S., Cellular automata as models of complexity. Nature, 311, (1984), 419-424.
[122]X. Yao, (1999), “Following the path of evolvable hardware,” Commun. ACM 42, 4 pp. 47-49.
[123]Zeigler, B.P. Theory of Modeling and Simulation (Wiley, New York, 1976).
[124]Zeigler, B.P. Multifacetted Modeling and Discrete Event Simulation (Academic Press, New York, 1984).
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔