Learning with monotonicity requirements for optimal routing with end-to-end quality of service constraints
نویسندگان
چکیده
In this paper, we adapt the classical learning algorithm for feed-forward neural networks when monotonicity is required in the inputoutput mapping. Monotonicity can be imposed by adding of suitable penalization terms to the error function. This yields a computationally efficient algorithm with little overhead compared to back-propagation. This algorithm is used to train neural networks for delay evaluation in an optimization scheme for optimal routing in a communication network.
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