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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© 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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