A abordagem para resolver o problema de controle de locomoção em animais pode ser hierárquica e modular. A estrutura geral de controle de locomoção, biologicamente, pode ser dividida em três partes: sistema nervoso central de alto-nível, sistema nervoso central de baixo-nível (CPG) e realimentação. Este conhecimento biológico pode ser utilizado para guiar
as novas direções de exploração do problema de controle de locomoção em robôs. O sistema nervoso central de alto-nível, por exemplo, determina o início da locomoção, a direção e a velocidade. O CPG controla as extensões, flexões e coordena todas as articulações (WU et al.,
2009). Dois pontos importantes que devem ser aprimorados no trabalho desenvolvido nesta Tese são: o gerenciamento de marchas; e a resposta aos sensores presentes no corpo do robô.
A camada de gerenciamento de marchas deve ser capaz de determinar a velocidade e a direção através da marcha atual do robô. Para tanto, um estudo mais profundo de mecanismos de geração de marchas e controle de direção deve ser realizado. Pois, mesmo com os avanços no método de aprendizagem e na generalização de marchas, esta Tese não apresentou nenhum estudo sobre a aprendizagem de movimentos para o robô movimentar-se em uma direção qualquer.
A utilização de sensores é fundamental para manter o equilíbrio do corpo e a estabilidade do deslocamento em diferentes tipos de terrenos. Um exemplo de grande sucesso na utilização de sensores é o BigDog (RAIBERT et al.,2008), um robô de quatro patas com cerca de 50 sensores e com habilidade de equilíbrio surpreendente1. A elaboração de mecanismos para a integração da abordagem proposta com sensores tem sido estudada. Neste sentido, o experimento com malha fechada pode ser aprimorado com a inclusão de sensores de toque ou pressão nos pés do robô para identificar com precisão a fase de apoio e a fase de balanço. Uma nova abordagem proposta deverá ser capaz de identificar as fase de apoio e balanço durante a aprendizagem. Assim, aprimorando os mecanismos de auto-adaptação de SOM-CSTG, o robô deverá ser capaz de adaptar melhor o movimento de suas pernas a diferentes terrenos irregulares.
108 108 108
REFERÊNCIAS
ACEBRóN, J. A. et al. The Kuramoto model: a simple paradigm for synchronization phenomena. Reviews of Modern Physics, [S.l.], v.77, n.1, p.137–185, Apr 2005.
AHMAD, A.; CHEN, L. I.; MOHAMAD, F. Simulation of stable-adaptive control of robot arm using self-organizing neural network. In: RESEARCH AND DEVELOPMENT, 2002.
SCORED 2002. STUDENT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2002. p.162–164. AMARASIRI, R.; ALAHAKOON, D.; SMITH, K. A. HDGSOM: a modified growing self-organizing map for high dimensional data clustering. In: HYBRID INTELLIGENT SYSTEMS, 2004. HIS’04. FOURTH INTERNATIONAL CONFERENCE ON. Anais. . . [S.l.: s.n.], 2004. p.216–221.
AMARASIRI, R. et al. HDGSOMr: a high dimensional growing self-organizing map using randomness for efficient web and text mining. In: WEB INTELLIGENCE, 2005.
PROCEEDINGS. THE 2005 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON. Anais. . . [S.l.: s.n.], 2005. p.215–221.
ANGENIOL, B.; LA CROIX VAUBOIS, G. de; LE TEXIER, J.-Y. Self-organizing feature maps and the travelling salesman problem. Neural Networks, [S.l.], v.1, n.4, p.289–293, 1988. AOKI, T.; AOYAGI, T. Self-organizing maps with asymmetric neighborhood function. Neural computation, [S.l.], v.19, n.9, p.2515–2535, 2007.
ARAI, Y.; HAKURA, J. Teleoperation system for real world robots-adaptive robot navigation based on sensor fusion. In: PARALLEL AND DISTRIBUTED SYSTEMS: WORKSHOPS, SEVENTH INTERNATIONAL CONFERENCE ON, 2000. Anais. . . [S.l.: s.n.], 2000. p.487–492.
ARAúJO, A. F. R.; SANTANA JR, O. V. Self-Organizing Map With Time-Varying Structure to Plan and Control Artificial Locomotion. Neural Networks and Learning Systems, IEEE Transactions on, [S.l.], 2014.
ARENA, P. et al. Cellular neural networks to explore complexity. Soft Computing - A Fusion of Foundations, Methodologies and Applications, [S.l.], v.1, p.120 – 136, 09 1997.
ARENA, P. et al. An adaptive, self-organizing dynamical system for hierarchical control of bio-inspired locomotion. Systems, Man, and Cybernetics, Part B, IEEE Transactions on, [S.l.], v.34, n.4, p.1823–1837, Aug. 2004.
ARENA, P.; FORTUNA, L.; BRANCIFORTE, M. Reaction-diffusion CNN algorithms to generate and control artificial locomotion. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, [S.l.], v.46, n.2, p.253–260, Feb 1999.
ARENA, P.; FORTUNA, L.; FRASCA, M. Multi-template approach to realize central pattern generators for artificial locomotion control. International Journal of Circuit Theory and Applications, [S.l.], v.30, p.441 – 458, 2002.
ARGALL, B. D. et al. A survey of robot learning from demonstration. Robotics and Autonomous Systems, [S.l.], v.57, n.5, p.469–483, May 2009.
ASAMIZU, T.; KOBAYASHI, Y. Acquisition of Body and Object Representation Based on Motion Learning and Planning Framework. In: INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, 2009. ISDA’09. NINTH INTERNATIONAL CONFERENCE ON. Anais. . . [S.l.: s.n.], 2009. p.1312–1317.
AYERS, J.; WITTING, J. Biomimetic approaches to the control of underwater walking
machines. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, [S.l.], v.365, n.1850, p.273–295, 2007.
BANERJEE, B. String tightening as a self-organizing phenomenon. Neural Networks, IEEE Transactions on, [S.l.], v.18, n.5, p.1463–1471, 2007.
BARRETO, G. A.; ARAÚJO, A. F. Predictive modeling and planning of robot trajectories using the self-organizing map. In: Innovations in Applied Artificial Intelligence. [S.l.]: Springer, 2004. p.1156–1165.
BARRETO, G. A.; ARAUJO, A. F. R. A self-organizing NARX network and its application to prediction of chaotic time series. In: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS. Proceedings. . . [S.l.: s.n.], 2001. v.3, p.2144–2149.
BARRETO, G. A. et al. A distributed robotic control system based on a temporal self-organizing neural network. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, [S.l.], v.32, n.4, p.347–357, 2002.
PRESS, M. (Ed.). Autonomous Robots From Biological Inspiration to Implementation and Control. [S.l.]: Massachusetts Institute of Technology, 2005.
BENANTE, R. C.; ARAúJO, A. F. R. Self-organizing maps to generate state trajectories of manipulators. IEEE International Conference on Systems, Man and Cybernetics, ISIC., [S.l.], v.1, p.1590–1595, Oct. 2007.
BERGLUND, E. et al. Mapping between different kinematic structures without absolute positioning during operation. Electronics letters, [S.l.], v.48, n.18, p.1110–1112, 2012. BERGLUND, E.; SITTE, J. The parameterless self-organizing map algorithm. IEEE Transactions on Neural Networks, [S.l.], v.17, n.2, p.305–316, 2006.
BILLING, E. A. A Formalism for Learning from Demonstration. Journal of Behavioral Robotics, [S.l.], v.1, n.1, p.1–13, 2010.
BLACKMORE, J.; MIIKKULAINEN, R. Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map. In: NEURAL NETWORKS, 1993., IEEE INTERNATIONAL CONFERENCE ON. Anais. . . [S.l.: s.n.], 1993. p.450–455.
BONET, B.; GEFFNER, H. Planning as Heuristic Search. Artificial Intelligence, [S.l.], v.129, p.5–33, 2001.
BUCHLI, J.; IJSPEERT, A. J. Self-organized adaptive legged locomotion in a compliant quadruped robot. Autonomous Robots, [S.l.], v.25, n.4, p.331–347, July 2008.
BLUCHER, E. (Ed.). Controles Típicos de Equipamentos e Processos Industriais. [S.l.]: Edgard Blucher, 2010.
REFERÊNCIAS 110 CHAPPELIER, J. C.; GRUMBACH, A. A Kohonen map for temporal sequences. In: Neural Networks and Their Applications. Conference Proceedings. Marseille, France: Domaine Univ. Saint-Jerome, 1996. p.104–110.
CHAPPELL, G. J.; TAYLOR, J. G. The temporal Kohønen map. Neural networks, [S.l.], v.6, n.3, p.441–445, 1993.
CHEN, W. et al. Smooth transition between different gaits of a hexapod robot via a central pattern generators algorithm. Journal of Intelligent & Robotic Systems, [S.l.], v.67, n.3-4, p.255–270, Mar. 2012.
CHENG, G.; ZELL, A. Externally Growing Cell Structures for Pattern Classification. In: PROCEEDING OF THE ICSC SYMPOSIA ON NEURAL COMPUTATION (NC’2000) MAY 23-26, 2000 IN BERLIN, GERMANY. Anais. . . ICSC Academic Press, 2000.
CHEUNG, Y.-M.; LAW, L. Rival-Model Penalized Self-Organizing Map. Neural Networks, IEEE Transactions on, [S.l.], v.18, n.1, p.289–295, 2007.
CHOW, C. K.; YUEN, S. Y. Signal self organizing map. In: NEURAL NETWORKS, 2007. IJCNN 2007. INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2007. p.213–218.
CHOW, T. W. S.; WU, S. T. Cell-splitting grid: a self-creating and self-organizing neural network. Neurocomputing, [S.l.], v.57, p.373–387, Mar. 2004.
CHUA, L. et al. Autonomous cellular neural networks: a unified paradigm for pattern formation and active wave propagation. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications,, [S.l.], v.42, n.10, p.559–577, Oct 1995.
CHUA, L.; ROSKA, T. The CNN paradigm. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications,, [S.l.], v.40, n.3, p.147–156, Mar 1993.
CHUA, L.; YANG, L. Cellular neural networks: theory. Circuits and Systems, IEEE Transactions on, [S.l.], v.35, n.10, p.1257–1272, Oct 1988.
CHUA, L.; YANG, L. Cellular neural networks: applications. Circuits and Systems, IEEE Transactions on, [S.l.], v.35, n.10, p.1273–1290, Oct 1988.
CORCHADO, E.; BARUQUE, B. WeVoS-ViSOM: an ensemble summarization algorithm for enhanced data visualization. Neurocomputing, [S.l.], v.75, n.1, p.171–184, 2012.
DALLE MOLE, V. L.; ARAÚJO, A. F. R. Growing self-organizing surface map: learning a surface topology from a point cloud. Neural computation, [S.l.], v.22, n.3, p.689–729, 2010. DALLEMOLE, V. L.; ARAÚJO, A. The growing self-organizing surface map. In: NEURAL NETWORKS, 2008. IJCNN 2008.(IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE). IEEE INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2008. p.2061–2068.
DATTA, A.; PAL, T.; PARUI, S. K. A modified self-organizing neural net for shape extraction. Neurocomputing, [S.l.], v.14, n.1, p.3–14, 1997.
EKEBERG Örjan. A combined neuronal and mechanical model of fish swimming. Biological Cybernetics, [S.l.], v.69, p.363 – 374, 1993.
EULIANO, N. R.; PRINCIPE, J. C. Spatio-temporal self-organizing feature maps. In: ICNN 96.
The 1996 IEEE International Conference on Neural Networks. New York, NY, USA: IEEE,
1996. v.4, p.1900–1905.
EULIANO, N. R.; PRINCIPE, J. C. A Spatio-Temporal Memory Based on SOMs with Activity Diffusion. In: OJA, E.; KASKI, S. (Ed.). Kohonen Maps. Amsterdam: Elsevier, 1999.
p.253–266.
FAIGL, J.; P ˇREU ˇCIL, L. Inspection planning in the polygonal domain by Self-Organizing Map. Applied Soft Computing, [S.l.], v.11, n.8, p.5028–5041, 2011.
FITZHUGH, R. Impulses and Physiological States in Theoretical Models of Nerve Membrane. Biophysical Journal, [S.l.], v.1, p.445 – 466, 1961.
FLENTGE, F. Locally Weighted Interpolating Growing Neural Gas. Neural Networks, IEEE Transactions on, [S.l.], v.17, n.6, p.1382–1393, 2006.
FRITZKE, B. Growing cell structures–A self-organizing network for unsupervised and supervised learning. Neural Networks, [S.l.], v.7, n.9, p.1441 – 1460, 1994.
FRITZKE, B. A Growing Neural Gas Network Learns Topologies. In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 7. Anais. . . MIT Press, 1995. p.625–632.
FRITZKE, B. Growing grid a self-organizing network with constant neighbourhood range and adaptation strength. Neural Processing Letters, [S.l.], v.2, n.5, p.9–13, Sept. 1995.
GÖPPERT, J.; ROSENSTIEL, W. Interpolation in SOM: improved generalization by iterative methods. Int. Conf. on Artificial Neural Networks, [S.l.], 1995.
GOPPERT, J.; ROSENSTIEL, W. The continuous interpolating self-organizing map. Neural Processing Letters, [S.l.], v.5, n.3, p.185–192, 1997.
GRAHAM, J.; STARZYK, J. A. A hybrid self-organizing Neural Gas based network. In: IJCNN 2008. Anais. . . [S.l.: s.n.], 2008. p.3806–3813.
HADZIC, F.; DILLON, T. S. CSOM: self-organizing map for continuous data. In: {IEEE} INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS {INDIN}, 3. Anais. . . IEEE: Piscataway: NJ: USA, 2005. p.740–745.
HADZIC, F.; DILLON, T. S. CSOM for Mixed Data Types. In: Advances in Neural Networks–ISNN 2007. [S.l.]: Springer, 2007. p.965–978.
HAESE, K. Self-organizing feature maps with self-adjusting learning parameters. IEEE Transactions on Neural Networks, [S.l.], v.9, n.6, p.1270–1278, 1998.
HAESE, K. Kalman filter implementation of self-organizing feature maps. Neural Computation, [S.l.], v.11, p.1211–1233, 1999.
HAGIWARA, M. Self-organizing feature map with a momentum term. Neurocomputing, [S.l.], v.10, n.1, p.71–81, Jan. 1996.
REFERÊNCIAS 112 HE, Y.; XU, S.; MIRANKER, W. L. A Force Field Driven SOM for boundary detection. In: NEURAL NETWORKS (IJCNN), THE 2010 INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2010. p.1–7.
HECHT-NIELSEN, R. Counterpropagation networks. Applied optics, [S.l.], v.26, n.23, p.4979–4983, 1987.
HIRAOKA, K.; AOYAGI, S. Path Searching of a Robot Manipulator Using Reinforcement Learning and Self-Organizing Maps. In: Service Robotics and Mechatronics. [S.l.]: Springer, 2010. p.341–346.
HIROSE, A.; NAGASHIMA, T. Predictive self-organizing map for vector quantization of migratory signals and its application to mobile communications. Neural Networks, IEEE Transactions on, [S.l.], v.14, n.6, p.1532–1540, 2003.
HODGKIN, A. L.; HUXLEY, A. F. A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, [S.l.], v.117, p.500 – 544, 1952.
HOLMES, P. et al. The Dynamics of Legged Locomotion: models, analyses, and challenges. SIAM Review, [S.l.], v.48, n.2, p.207–304, 2006.
HORIO, K.; YAMAKAWA, T. Adaptive Self-Organizing Relationship Network and Its Application to Adaptive Control. In: INTERNATIONAL CONFERENCE ON SOFT COMPUTING, IIZUKA2000, IIZUKA, FUKUOKA, JAPAN, OCTOBER 1–4, 2000, 6. Anais. . . [S.l.: s.n.], 2000. p.299–304.
HORIO, K.; YAMAKAWA, T. Feedback self-organizing map and its application to spatio-temporal pattern classification.
International-Journal-of-Computational-Intelligence-and-Applications, [S.l.], v.1, p.1–18, 2001.
HOU, Z.-S.; WANG, Z. From model-based control to data-driven control: survey, classification and perspective. Information Sciences, [S.l.], v.235, p.3–35, 2013.
HU, Y. C. et al. Grey self-organizing feature maps. Neurocomputing, [S.l.], v.48, Oct. 2002. HUANG, D.; YI, Z. Shape recovery by a generalized topology preserving SOM.
Neurocomputing, [S.l.], v.72, n.1-3, p.573–580, 2008.
HÜSER, M.; ZHANG, J. Visual programming by demonstration of grasping skills in the context of a mobile service robot using 1D-topology based self-organizing-maps. Robotics and Autonomous Systems, [S.l.], v.60, n.3, p.463–472, 2012.
IGLESIAS, R.; BARRO, S. SOAN: self-organizing with adaptive neighborhood neural network. In: FOUNDATIONS AND TOOLS FOR NEURAL MODELING. INTERNATIONAL
WORK-CONFERENCE ON ARTIFICIAL AND NATURAL NEURAL NETWORKS, IWANN’99. PROCEEDINGS, (LECTURE NOTES IN COMPUTER SCIENCE VOL.1606), Berlin, Germany. Anais. . . Springer-Verlag, 1999. v.1, p.591–600.
IJSPEERT, A. A connectionist central pattern generator for the aquatic and terrestrial gaits of a simulated salamander. Biological Cybernetics, [S.l.], v.84, n.5, p.331–348, 2001.
IJSPEERT, A. et al. From swimming to walking with a salamander robot driven by a spinal cord model. Science, [S.l.], v.315, n.5817, p.1416–1420, 2007.
IJSPEERT, A.; HALLAM, J.; WILLSHAW, D. Evolving swimming controllers for a simulated lamprey with inspiration from Neurobiology. Adaptive Behavior, [S.l.], v.7, n.2, p.151–172, 1999.
IJSPEERT, A. J. Central pattern generators for locomotion control in animals and robots: a review. Neural Networks, Oxford, UK, UK, v.21, n.4, p.642–653, 2008.
IJSPEERT, A. J.; KODJABACHIAN, J. Evolution and development of a central pattern generator for the swimming of a Lamprey. Artitifical Life, Cambridge, MA, USA, v.5, n.3, p.247–269, 1999.
ISHII, K. et al. A navigation system for an underwater vehicle using the self-organizing map. In: INTERNATIONAL OFFSHORE AND POLAR ENGINEERING CONFERENCE.
Proceedings. . . [S.l.: s.n.], 2002. p.284–289.
ISHII, K.; YANO, K. Path planning system for a mobile robot using self-organizing map. In: INFO-TECH AND INFO-NET, 2001. PROCEEDINGS. ICII 2001-BEIJING. 2001
INTERNATIONAL CONFERENCES ON. Anais. . . [S.l.: s.n.], 2001. v.4, p.32–37.
KOENIG, N.; HOWARD, A. Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: IN IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS. Anais. . . [S.l.: s.n.], 2004. v.3, p.2149–2154.
KOHONEN, T. Self-organized formation of topologically correct feature maps. Biological Cybernetics, [S.l.], v.43, p.59–69, 1982.
KOHONEN, T. The self-organizing map. Neurocomputing, [S.l.], v.21, n.1-3, p.1 – 6, 1998. KOHONEN, T. Fast evolutionary learning with batch-type self-organizing maps. Neural Processing Letters, [S.l.], v.9, p.153–162, 1999.
KOHONEN, T.; HARI, R. Where the abstract feature maps of the brain might come from. Trends in Neurosciences, [S.l.], v.22, p.135–139, 1999.
KOIKKALAINEN, P.; VARSTA, M. Robot path generation for surface processing applications via neural. 1996. 228–238p. v.2904.
KOUTNÍK, J.; ŠNOREK, M. Temporal hebbian self-organizing map for sequences. In: Artificial Neural Networks-ICANN 2008. [S.l.]: Springer, 2008. p.632–641.
KUMAR, S. et al. Visual motor control of a 7DOF redundant manipulator using redundancy preserving learning network. Robotica, [S.l.], v.28, p.795–810, 2010.
KUMAR, S.; PATEL, N.; BEHERA, L. Visual motor control of a 7 DOF robot manipulator using function decomposition and sub-clustering in configuration space. Neural Processing Letters, [S.l.], v.28, n.1, p.17–33, 2008.
Kwong-Sak-Leung, Hui-Dong-Jin, Z.-B.-X. An expanding self-organizing neural network for the traveling salesman problem. Theoretical Computer Science. 29 Nov. 2004; 328(1–2): 267–92, [S.l.], 2004.
REFERÊNCIAS 114 LANG, R.; WARWICK, K. The plastic self organising map. In: NEURAL NETWORKS, 2002. IJCNN’02. PROCEEDINGS OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2002. v.1, p.727–732.
LEE, J. A.; VERLEYSEN, M. Self-organizing maps with recursive neighborhood adaptation. Neural networks : the official Journal of the International Neural Network Society, [S.l.], 2002.
LEE, Y.; KIM, S.; LEE, J. Data-driven biped control. ACM Transactions on Graphics (TOG), [S.l.], v.29, n.4, p.129, 2010.
LI, C.; LOWE, R.; ZIEMKE, T. A Novel Approach to Locomotion Learning: actor-critic architecture using central pattern generators and dynamic motor primitives. Frontiers in Neurorobotics, [S.l.], v.8, n.23, 2014.
LIOU, C. Y.; KUO, Y. T. Conformal self-organizing map for a genus-zero manifold. Visual Computer, [S.l.], v.21, n.5, p.340–353, June 2005.
LIOU, C.-Y.; KUO, Y.-T.; HUANG, J.-C. Conformal self-organizing map on curved seamless surface. Neurocomputing, [S.l.], v.71, n.16-18, p.3140–3149, 2008.
LIOU, C. Y.; TAI, W. P. Conformal self-organization for continuity on a feature map. Neural Networks, [S.l.], v.12, n.6, p.893–905, 1999.
LUDWIG, L. et al. SOM with topological interpolation for the prediction of interference spectra. [S.l.]: Finnsh Arti cial Intelligence Society., 1995.
MALMSTROM, K.; SITTE, J.; ISKE, B. Perception-stimulated generation of simple robot navigation behavior. In: INTELLIGENT SYSTEMS AND SMART MANUFACTURING. Anais. . . [S.l.: s.n.], 2001. p.228–239.
MARSLAND, S.; SHAPIRO, J.; NEHMZOW, U. A self-organising network that grows when required. Neural Netw., Oxford, UK, UK, v.15, n.8-9, p.1041–1058, 2002.
MASUTTI, T. A. S.; CASTRO, L. N. de. Neuro-immune approach to solve routing problems. Neurocomputing, [S.l.], v.72, n.10-12, p.2189–2197, 2009.
MATSUOKA, K. Mechanisms of frequency and pattern control in the neural rhythm generators. Biological Cybernetics, [S.l.], v.56, p.345 – 353, 1987.
MATTONE, R. The growing neural map: an on-line competitive clustering algorithm. In: ROBOTICS AND AUTOMATION, 2002. PROCEEDINGS. ICRA’02. IEEE
INTERNATIONAL CONFERENCE ON. Anais. . . [S.l.: s.n.], 2002. v.4, p.3888–3893. MAUFROY, C.; KIMURA, H.; TAKASE, K. Towards a general neural controller for quadrupedal locomotion. Neural Networks, [S.l.], v.21, p.667 – 681, 2008.
MAUFROY, C.; KIMURA, H.; TAKASE, K. Integration of posture and rhythmic motion controls in quadrupedal dynamic walking using phase modulations based on leg
loading/unloading. Autonomous Robots, [S.l.], v.28, p.331 – 353, 2010.
UNIVERSITY, P. (Ed.). Muscles, Reflexes, and Locomotion. [S.l.]: Princeton University Press, 1984.
NAGUMO, J.; ARIMOTO, S.; YOSHIZAWA, S. An Active Pulse Transmission Line Simulating Nerve Axon. Proceedings of the IRE, [S.l.], v.50, p.2061 – 2070, 2007.
NAKAMURA, Y. et al. Reinforcement learning for a biped robot based on a CPG-actor-critic method. Neural Networks, Oxford, UK, UK, v.20, n.6, p.723–735, 2007.
NEME, A. et al. Self-Organizing Maps with Non-cooperative Strategies (SOM-NC). In: Advances in Self-Organizing Maps. [S.l.]: Springer, 2009. p.200–208.
NI, H.; YIN, H. A self-organising mixture autoregressive network for FX time series modelling and prediction. Neurocomputing, [S.l.], v.72, n.16 - 18, p.3529–3537, 2009.
NISHIDA, S.; ISHII, K.; FURUKAWA, T. Self-organizing decision-making system for AUV. In: UNDERWATER TECHNOLOGY AND WORKSHOP ON SCIENTIFIC USE OF
SUBMARINE CABLES AND RELATED TECHNOLOGIES, 2007. SYMPOSIUM ON. Anais. . . [S.l.: s.n.], 2007. p.506–511.
NISHIDA, S.; ISHII, K.; FURUKAWA, T. An adaptive neural network control system using mnSOM. In: OCEANS 2006-ASIA PACIFIC. Anais. . . [S.l.: s.n.], 2007. p.1–6.
NISSINEN, A. S.; HYöTYNIEMI, H. Evolutionary self-organizing map. In: EUROPEAN CONGRESS ON INTELLIGENT TECHNIQUES AND SOFT COMPUTING. EUFIT ’98, 6., Aachen, Germany. Anais. . . Verlag Mainz, 1998. v.3, p.1596–1600.
OCSA, A.; BEDREGAL, C.; CUADROS-VARGAS, E. DB-GNG: a constructive self-organizing map based on densilty. In: NEURAL NETWORKS, 2007. IJCNN 2007. INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2007. p.1953–1958. OHTA, R.; SAITO, T. A Growing Self-Organizing Algorithm for Dynamic Clustering. In: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, WASHINGTON, DC, USA, JULY 15–19. Anais. . . [S.l.: s.n.], 2001.
OHTSUKA, A. et al. Self-organizing map based on block learning. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, [S.l.], v.E88A, n.11, p.3151–3160, Nov. 2005.
PADOAN JUNIOR, A. C.; BARRETO, G. D. A.; ARAUJO, A. F. R. Modeling and production of robot trajectories using the Temporal Parametrized Self Organizing Maps. International Journal of Neural Systems, [S.l.], v.13, n.2, p.119–127, Apr. 2003.
RAIBERT, M. et al. Bigdog, the rough-terrain quadruped robot. In: IFAC WORLD CONGRESS, 2008, 17. Proceedings. . . [S.l.: s.n.], 2008. v.17, n.1, p.10822–10825.
REEVE, R.; HALLAM, J. An analysis of neural models for walking control. Neural Networks, IEEE Transactions on, [S.l.], v.16, n.3, p.733–742, May 2005.
REGO, R. do; ARAÚJO, A. F.; LIMA NETO, F. B. de. Growing self-organizing maps for surface reconstruction from unstructured point clouds. In: NEURAL NETWORKS, 2007. IJCNN 2007. INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2007. p.1900–1905.
REGO, R. L. do; ARAÚJO, A. F. A surface reconstruction method based on self-organizing maps and intrinsic delaunay triangulation. In: NEURAL NETWORKS (IJCNN), THE 2010 INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2010. p.1–8.
REFERÊNCIAS 116 REGO, R. M. E.; ARAUJO, A. F. R.; LIMA NETO, F. de. Growing Self-Reconstruction Maps. Neural Networks, IEEE Transactions on, [S.l.], v.21, n.2, p.211–223, 2010.
RESSOM, H.; WANG, D.; NATARAJAN, P. Adaptive double self-organizing map and its application in gene expression data. In: NEURAL NETWORKS, 2003. PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2003. v.1, p.39–44. RIGHETTI, L.; BUCHLI, J.; IJSPEERT, A. J. Adaptive Frequency Oscillators and Applications. The Open Cybernetics & Systemics Journal, [S.l.], v.3, n.2, p.64–69, Oct. 2009.
RIGHETTI, L.; IJSPEERT, A. Design methodologies for central pattern generators: an application to crawling humanoids. In: PROCEEDINGS OF ROBOTICS: SCIENCE AND SYSTEMS, Philadelphia, USA. conference. . . [S.l.: s.n.], 2006. p.191–198.
RITTER, H. Parametrized self-organizing maps. In: ICANN’93. [S.l.]: Springer, 1993. p.568–575.
SAAVEDRA, C. et al. Fusion of self organizing maps. In: Computational and Ambient Intelligence. [S.l.]: Springer, 2007. p.227–234.
SAKURAI, N.; HATTORI, M.; ITO, H. SOM associative memory for temporal sequences. In: NEURAL NETWORKS, 2002. IJCNN’02. PROCEEDINGS OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2002. v.1, p.950–955.
SALEM, Z. N. B.; MOURIA-BEJI, F.; KAMOUN, F. Spatio-temporal organization map: a speech recognition application. In: ARTIFICIAL NEURAL NETWORKS: BIOLOGICAL INSPIRATIONS - {ICANN} 2005, PT. 1, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE. Anais. . . [S.l.: s.n.], 2005. p.371–378.
SANTANA JR, O. V. Mapa Auto-Organizável para Controle e Gerenciamento de Locomoção Artificial. 2010. Dissertação (Mestrado em Ciência da Computação) — UFPE. SANTANA JR, O. V. Vídeo da Simulação do Robô Cachorro com Dados D2. 2014. SANTANA JR, O. V. Vídeo da Simulação do Robô Cachorro com Dados D1. 2014. SANTANA JR, O. V.; ARAUJO, A. F. R. A Self-Organizing Map for Controlling Artificial Locomotion. In: DIAMANTARAS, K.; DUCH, W.; ILIADIS, L. (Ed.). Artificial Neural Networks - ICANN 2010. [S.l.]: Springer Berlin Heidelberg, 2010. p.420–425. (Lecture Notes in Computer Science, v.6353).
SANTOS, C. P.; MATOS, V. Gait transition and modulation in a quadruped robot: a
brainstem-like modulation approach. Robotics and Autonomous Systems, [S.l.], v.59, n.9, p.620–634, Sept. 2011.
SANTOS, P. G. D. et al. A six-legged robot-based system for humanitarian demining missions. Mechatronics, [S.l.], v.17, n.8, p.417 – 430, 2007.
SENIN, P. Dynamic Time Warping Algorithm Review. [S.l.]: Information and Computer Science Department - University of Hawaii at Manoa, 2008.
SHAH-HOSSEINI, H. Binary tree time adaptive self-organizing map. Neurocomputing, [S.l.], v.74, n.11, p.1823–1839, 2011.
SHAH-HOSSEINI, H.; SAFABAKHSH, R. TASOM: the time adaptive self-organizing map. In: INFORMATION TECHNOLOGY CODING AND COMPUTING 2000 PROCEEDINGS INTERNATIONAL CONFERENCE ON. Anais. . . [S.l.: s.n.], 2000. p.422–427.
SHAH-HOSSEINI, H.; SAFABAKHSH, R. Automatic multilevel thresholding for image segmentation by the growing time adaptive self-organizing map. {IEEE} Transactions on Pattern Analysis and Machine Intelligence, [S.l.], v.24, n.10, p.1388–1393, Oct. 2002. SHAH-HOSSEINI, H.; SAFABAKHSH, R. Tasom: a new time adaptive self-organizing map. {IEEE} Transactions on Systems Man and Cybernetics, Part B: Cybernetics, [S.l.], v.33,
n.2, p.271–282, Apr. 2003.
SHIMADA, A. et al. Robust estimation of human posture using incremental learnable Self-Organizing Map. In: NEURAL NETWORKS, 2008. IJCNN 2008.(IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE). IEEE INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2008. p.939–946.
PRESS, T. M. (Ed.). Introduction to Autonomous Mobile Robots. [S.l.]: The MIT Press, 2004.
SONS, J. W. . (Ed.). Robot Modeling and Control. 1.ed. [S.l.]: John Wiley & Sons, 2006. SPROEWITZ, A. et al. Learning to Move in Modular Robots using Central Pattern Generators and Online Optimization. International Journal of Robotics Research, Thousand Oaks, CA, USA, v.27, n.3-4, p.423–443, 2008.
SRINIVASA, N.; GROSSBERG, S. A self-organizing neural model for fault-tolerant control of redundant robots. In: NEURAL NETWORKS, 2007. IJCNN 2007. INTERNATIONAL JOINT CONFERENCE ON. Anais. . . [S.l.: s.n.], 2007. p.483–488.
SRINIVASA, N.; GROSSBERG, S. A head-neck-eye system that learns fault-tolerant saccades to 3-D targets using a self-organizing neural model. Neural Netw., Oxford, UK, UK, v.21, n.9, p.1380–1391, nov 2008.
STEIN, R. B. et al. Improved neuronal models for studying neural networks. Biological Cybernetics, [S.l.], v.15, p.1–9, 1973.
STRICKERT, M.; HAMMER, B. Merge SOM for temporal data. Neurocomputing, [S.l.], v.64, p.39–71, Mar. 2005.
SU, M.-C.; CHANG, H.-T. New model of self-organizing neural networks and its application in data projection. IEEE Transactions on Neural Networks, Piscataway, NJ, v.12, n.1,
p.153–158, Jan. 2001.
TOKUNAGA, K.; KAWABATA, N.; FURUKAWA, T. Self Evolving Modular Network. IEICE
TRANSACTIONS on Information and Systems, [S.l.], v.95, n.5, p.1506–1518, 2012.
TOPOLOGY-PRESERVING INTERPOLATION IN SELF-ORGANIZING MAPS, Nanterre, France, 10 1993. EC2. Anais. . . Neuro Nimes, 1993.
VARSTA, M. et al. Temporal Kohonen map and the recurrent self-organizing map: analytical and experimental comparison. Neural Processing Letters, [S.l.], v.13, n.3, p.237–251, June 2001.
REFERÊNCIAS 118 VENKATESH, Y.-V.; KUMAR RAJA, S.; RAMYA, N. Multiple contour extraction from graylevel images using an artificial neural network. Image Processing, IEEE Transactions on, [S.l.], v.15, n.4, p.892–899, 2006.
VOEGTLIN, T. Context quantization and Contextual Self-Organizing Maps. In:
INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, Piscataway, NJ.