Abstract
In contrast to most applications, it is not suitable for autonomous agents to distinguish between a learning and a performance phase; rather continuous learning is required, especially in dynamically changing, partially unknown environments. This paper shows how modified radial basis function networks can be used as controllers for mobile robots that can adapt to different environments and also to sensor faults. In addition, the proposed model yields fast convergence rates in various regression and classification tasks, e.g., learning the well-known double-spiral problem requires only one epoch with perfect generalization.
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References
Y.S., Abu-Mostafa, Hints and the VC Dimension, Neural Computation, 1992.
H. Bourlard and N. Morgan, Connectionist Speech Recognition-A Hybrid Approach, Kluwer Academic Publishers, 1994.
S.E. Fahlman and C. Lebiere, The Cascade-Correlation Learning Architecture, In D. Touretzky (ed.), Advances in Neural Information Processing Systems 2, pp. 524–532, Morgan Kaufmann Publishers, 1990.
B. Fritzke, Supervised Learning with Growing Cell Structures, In J. Cowan, G. Tesauro, and J. Alspector (eds.), Advances in Neural Information Processing Systems 6, pp. 255–262, Morgan Kaufmann Publishers, 1994.
Khepera Users Manual, Laboratoire de microinformatique, Swiss Federal Institute of Technology (EPFL), 1015 Lausanne, Switzerland.
Kohonen, T. (1989). Self-Organization and Associative Memory. Springer-Verlag.
K.J. Lang and M.J. Witbrock, Learning to tell two spirals apart, In D. Touretzky, G. Hinton, and T. Sejnowski (eds.), Proceedings of the 1988 Connectionist Models Summer School, pp. 52–59, Morgan Kaufmann Publishers, 1989.
Y. Le Cun, Generalization and Network Design Strategies. In R. Pfeifer, Z. Schreter, F. Fogelman, and L. Steels (eds.), Connectionism in Perspective, Zurich, pp. 148–153., Elsevier, Amsterdam, 1989.
Y. Le Cun, J.S., Denker, and S.A. Solla, Optimal Brain Damage, In D. Touretzky (ed.), Advances in Neural Information Processing Systems 2, pp. 598–605, Morgan Kaufmann Publishers, 1990.
J. Moody and C. Darken, Learning with Localized Receptive Fields. In D. Touretzky, G. Hinton, and T. Sejnowski (eds.), Proceedings of the 1988 Connectionist Models Summer School, pp. 133–143, Morgan Kaufmann Publishers, 1989.
M.C. Mozer and P Smolensky, Skeletonization: A technique for trimming the fat from a network via relevance assessment, In D.S. Touretzky (ed.), Advances in Neural Information Processing Systems 1, pp. 107–115, Morgan Kaufmann, 1989.
P. Simard, B. Victori, Y. Le Cun, and J. Denker, Tangent Prop — A formalism for specifying selected invariances in an adaptive network, In David S. Touretzky (ed.), Advances in Neural Information Processing Systems 4, pp. 895–903, Morgan Kaufmann Publishers, 1992.
V. Vapnik, Estimation of Dependencies Based on Empirical Data, Springer, 1982.
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© 1997 Springer-Verlag Berlin Heidelberg
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Salomon, R. (1997). The application of radial basis function networks with implicit continuity constraints. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020253
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DOI: https://doi.org/10.1007/BFb0020253
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