Multilayer feedforward networks are universal approximators
Neural Networks
Neural Computation
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Comparison of Neural Networks Incorporating Partial Monotonicity by Structure
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Calibrating artificial neural networks by global optimization
Expert Systems with Applications: An International Journal
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Multi-layer perceptron networks as universal approximators are well-known methods for system identification. For many applications a multi-dimensional mathematical model has to guarantee the monotonicity with respect to one or more inputs. We introduce the MONMLP which fulfils the requirements of monotonicity regarding one or more inputs by constraints in the signs of the weights of the multi-layer perceptron network. The monotonicity of the MONMLP does not depend on the quality of the training because it is guaranteed by its structure. Moreover, it is shown that in spite of its constraints in signs the MONMLP is a universal approximator. As an example for model predictive control we present an application in the steel industry.