Reliable Integration of Continuous Constraints into Extreme Learning Machines

Neumann K, Rolf M, Steil JJ (2013)
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21(Suppl 2): 35-50.

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Journal Article | Published | English
Abstract
The application of machine learning methods in the engineering of intelligent technical systems often requires the integration of continuous constraints like positivity, mono- tonicity, or bounded curvature in the learned function to guarantee a reliable perfor- mance. We show that the extreme learning machine is particularly well suited for this task. Constraints involving arbitrary derivatives of the learned function are effectively implemented through quadratic optimization because the learned function is linear in its parameters, and derivatives can be derived analytically. We further provide a construc- tive approach to verify that discretely sampled constraints are generalized to continuous regions and show how local violations of the constraint can be rectified by iterative re- learning. We demonstrate the approach on a practical and challenging control problem from robotics, illustrating also how the proposed method enables learning from few data samples if additional prior knowledge about the problem is available.
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Neumann K, Rolf M, Steil JJ. Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2013;21(Suppl 2):35-50.
Neumann, K., Rolf, M., & Steil, J. J. (2013). Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(Suppl 2), 35-50.
Neumann, K., Rolf, M., and Steil, J. J. (2013). Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, 35-50.
Neumann, K., Rolf, M., & Steil, J.J., 2013. Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 21(Suppl 2), p 35-50.
K. Neumann, M. Rolf, and J.J. Steil, “Reliable Integration of Continuous Constraints into Extreme Learning Machines”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 21, 2013, pp. 35-50.
Neumann, K., Rolf, M., Steil, J.J.: Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 21, 35-50 (2013).
Neumann, Klaus, Rolf, Matthias, and Steil, Jochen J. “Reliable Integration of Continuous Constraints into Extreme Learning Machines”. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21.Suppl 2 (2013): 35-50.
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