Abstract A scheme of expert system based on iterative learning control is proposed. Iterative learning control can obtain control experience from the historical data to build the knowledge base of expert system. Considering some uncertain factors, a flexible measure is adopted in iterative learning control (ILC). Simulation proves the feasibility and effect of the air conditioning control exper...

Relational databases are valuable sources for ontology learning. Methods and tools have been proposed to generate ontologies from such structured input. However, a major persisting limitation is the derivation of ontologies with flat structure that simply mirror the schema of the source databases. In this paper, we show how the RDBToOnto tool can be used to derive accurate ontologies by taking ...

In this paper it is proposed an extended memory iterative learning technique. The knowledge of the iterative learning controller can be built by using the previous tasks of the iterative learning controller in tracking various desired trajectories in terms of a database of input and output data. For a new desired trajectory, iterative learning controller can predict the initial control input fr...

BACKGROUND Policy initiatives and an increasing amount of the literature within higher education both call for students to become more involved in creating their own learning. However, there is a lack of studies in undergraduate nursing education that actively involve students in developing such learning material with descriptions of the students' roles in these interactive processes. METHOD ...

In many industrial robot applications it is a fact that the robot is programmed to do the same task repeatedly. By observing the control error in the different iterations of the same task it becomes clear that it is actually highly repetitive. Iterative Learning Control (ILC) allows to iteratively compensate for and, hence, remove this repetitive error. In the thesis different aspects of iterat...

In this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. This emotional l...

The present paper explores the link between thresholding, one of the key enablers in sparsity-promoting algorithms, and Volterra system identification in the context of time-adaptive or online learning. A connection is established between the recently developed generalized thresholding operator and optimization theory via the concept of proximal mappings which are associated with non-convex pen...