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A novel artificial hydrocarbon networks based value function approximation in hierarchical reinforcement learning
(Springer Verlag, 2017)
Reinforcement learning aims to solve the problem of learning optimal or near-optimal decision-making policies for a given domain problem. However, it is known that increasing the dimensionality of the input space (i.e. ...
Doubly fed induction generator (DFIG) wind turbine controlled by artificial organic networks
(Springer Verlag, 2017)
The main goal of this paper is to show the control capabilities of artificial organic networks when they are applied to variable speed wind generators. Since doubly fed induction generator (DFIG) is one of the most important ...
Interpretability of artificial hydrocarbon networks for breast cancer classification
(Institute of Electrical and Electronics Engineers Inc., 2017)
In machine learning, interpretability refers to understand the underlying behavior of the prediction of a model in order to identify diagnosis criteria and/or new rules from its output. Interpretability contributes to ...