An iterative learning identiication method is proposed for curve identiication problems. The basic idea is to convert the curve identiication problem into an optimal tracking control problem. The measured trajectories are regarded as the desired trajectories to be optimally tracked and the curve to be identiied is taken as a virtual control function. A high-order learning updating law is applie...

In this paper an iterative learning control design method is depicted, leading to a feedforward controller minimizing tracking error of repetitive trajectories. The approach is extended to the case of a fuzzy controller, where the plant inverse is approximated by a fuzzy system. This provides some extra features, being also suitable to be applied to nonlinear plants. A simple application illust...

Motivated by the task of hyperparameter optimization, we introduce the non-stochastic bestarm identification problem. Within the multiarmed bandit literature, the cumulative regret objective enjoys algorithms and analyses for both the non-stochastic and stochastic settings while to the best of our knowledge, the best-arm identification framework has only been considered in the stochastic settin...

Hidden Markov models have successfully been applied as models of discrete time series in many fields. Often, when applied in practice, the parameters of these models have to be estimated. The currently predominating identification methods, such as maximumlikelihood estimation and especially expectation-maximization, are iterative and prone to have problems with local minima. A non-iterative met...

Virtual learning circumstances have been observed as consistent growth over the years. The widespread use of online leads to an emerging amount enrollments, also from pupils who quit education scheme previously. However, it earned increased withdrawal rate when compared conventional classrooms. Quick identification is a difficult issue that can be alleviated with help previous models for data e...

This paper addresses the possibility of capacity withholding by energy producers, who seek to increase the market price and their own profits. The energy market is simulated as an iterative game, where each state game corresponds to an hourly energy auction with uniform pricing mechanism. The producers are modeled as agents that interact with their environment through reinforcement learning (RL...

The predominant learning strategy for H(S)MMs is local search heuristics, of which the Baum-Welch/ expectation maximization (EM) algorithm is mostly used. It is an iterative learning procedure starting with a predefined topology and randomly-chosen initial parameters. However, state-of-the-art approaches based on arbitrarily defined state numbers and parameters can cause the risk of falling int...

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind...

In this paper we propose an iterative learning control scheme based on the quasi-Newton method. The iterative learning control is designed to improve the performance of the systems working cyclically. We consider the general type of systems described by continuously diierentiable operator acting in Banach spaces. The suucient conditions for the convergence of quasi-Newton iterative learning alg...

In this paper, we propose an integrated policy learning framework that fuses iterative learning control (ILC) and reinforcement learning. Integration is accomplished at the exploration level of the reinforcement learning algorithm. The proposed algorithm combines fast convergence properties of iterative learning control and robustness of reinforcement learning. This way, the advantages of both ...