in this paper an adaptive pid controller for wind energy conversion systems (wecs) has been developed. theadaptation technique applied to this controller is based on reinforcement learning (rl) theory. nonlinearcharacteristics of wind variations as plant input, wind turbine structure and generator operational behaviordemand for high quality adaptive controller to ensure both robust stability an...

The Learning Objects Structure Petri Net, LOSPN describes the structure and mutual dependence of a set of learning objects, LOs. It allows to model the context of each learning object in terms of preconditions (prerequisites) and postconditions (learning objectives or learning targets). It is this property which makes re-use of learning objects in different courses and in different departments ...

INTRODUCTION The article GRAPHICAL MODELS: PARAMETER LEARNING discussed the learning of parameters for a xed graphical model. In this article, we discuss the simultaneous learning of parameters and structure. Real-world applications of such learning abound and can be found in (e.g.) the Proceedings of the Conference on Uncertainty in Arti cial Intelligence (1991 and after). An index to software...

Discriminative learning framework is one of the very successful fields of machine learning. The methods of this paradigm, such as Boosting, and Support Vector Machines have significantly advanced the state-of-the-art for classification by improving the accuracy and by increasing the applicability of machine learning methods. Recently there has been growing interest to generalize discrimative le...

General correlations between form and meaning at the level of argument structure patterns have often been assumed to be innate. Claims of innateness typically rest on the idea that the input is not rich enough for general learning strategies to yield the required representations. The present work demonstrates that the semantics associated with argument structure generalizations can indeed be le...

We describe a family of global optimization procedures that automatically decompose optimization problems into smaller loosely coupled problems. The solutions of these are subsequently combined with message passing algorithms. We show empirically that these methods produce better solutions with fewer function evaluations than existing global optimization methods. To develop these methods, we in...