ICANN 2008: Artificial Neural Networks - ICANN 2008 pp 597-606 | Cite as
Comparison of Neural Networks Incorporating Partial Monotonicity by Structure
Abstract
Neural networks applied in control loops and safety-critical domains have to meet more requirements than just the overall best function approximation. On the one hand, a small approximation error is required, on the other hand, the smoothness and the monotonicity of selected input-output relations have to be guaranteed. Otherwise the stability of most of the control laws is lost. Three approaches for partially monotonic models are compared in this article, namely Bounded Derivative Network (BDN) [1], Monotonic Multi-Layer Perceptron Network (MONMLP) [2], and Constrained Linear Regression (CLR). Authors investigated the advantages and disadvantages of these approaches related to approximation performance, training of the model and convergence.
Keywords
Root Mean Square Monotonic Behavior Hyperbolic Tangent Function High Nitric Oxide Small Approximation ErrorPreview
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