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Many real scenarios in machine learning are non-stationary. These challenges forces to develop new algorithms that are able to deal with changes in the underlying problem to be learnt. These changes can be gradual or abrupt. As the dynamics of the changes can be different, the existing machine learning algorithms exhibit difficulties to cope with them. In this work we propose a new method, that is...
A dynamic output feedback linearization algorithm for a model reference control of nonlinear multi-input multi-output (MIMO) systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is introduced. ANARX structure of the model can be obtained by training a neural network with a specific restricted connectivity structure. Linear discrete-time reference models are given in the...
A dynamic output feedback linearization technique for model reference control of nonlinear TITO (two-input two-output) systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is proposed. ANARX structure of the model can be obtained by training a neural network of the specific restricted connectivity structure. Linear discrete-time reference model is given in the form of...
This paper describes several new online model-free reinforcement learning (RL) algorithms. We designed three new reinforcement algorithms, namely: QV2, QVMAX, and QVMAX2, that are all based on the QV-learning algorithm, but in contrary to QV-learning, QVMAX and QVMAX2 are off-policy RL algorithms and QV2 is a new on-policy RL algorithm. We experimentally compare these algorithms to a large number...
We describe an ensemble of classifiers based approach for incrementally learning from new data drawn from a distribution that changes in time, i.e., data obtained from a nonstationary environment. Specifically, we generate a new classifier using each additional dataset that becomes available from the changing environment. The classifiers are combined by a modified weighted majority voting, where the...
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