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. ...

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 ...

Surveys on supervised machine show that each technique has strengths and weaknesses that make each of them more suitable for a particular domain or learning task. No technique is capable to tackle every supervised learning ...

Reinforcement learning in continuous states and actions has been limitedly studied in ocassions given difficulties in the determination of the transition function, lack of performance in continuous-to-discrete relaxation ...

Human activity recognition (HAR) aims to classify and identify activities based on data-driven from different devices, such as sensors or cameras. Particularly, mobile devices have been used for this recognition task. ...

Artificial hydrocarbon networks (AHN) is a supervised learning model that is loosely inspired on the interactions of molecules in organic compounds. This method is able to model data in a hierarchical and robust way. ...