In this paper, iterative learning control using output data, which are more advanced than the relative degree of the system, is being investigated. It is known that the output error can be made zero with the conventional iterative learning control in which the input is updated with the output data advanced by the relative degree. However, the input can become too large for nonminimum phase syst...

With the combination of the model reference adaptive control and iterative learning control, a model reference adaptive iterative learning control algorithm was proposed for a class of first order linear time-varying systems which are BIBO stable and repeatable in a finite time interval . By means of Lyapunov technique , an iterative learning control law with adaptive update law for time-varyin...

The present paper deals with a systematic study of incremental learning algorithms. The general scenario is as follows. Let c be any concept; then every in nite sequence of elements exhausting c is called positive presentation of c. An algorithmic learner successively takes as input one element of a positive presentation as well as its previously made hypothesis at a time, and outputs a new hyp...

Iterative learning control methods are represented as powerful tools to control dynamics nowadays. Our new controller based on particular case of iterative learning control is radically different from the presented conventional method, which attempts to stabilize a class of nonlinear systems by satisfying the conditions of Lyapunov Stability Theorem. Since our algorithm is model based, its robu...

In this paper, we systematically explore lexicalized and non-lexicalized local syntactic features for the task of Native Language Identification (NLI). We investigate different types of feature representations in singleand cross-corpus settings, including two representations inspired by a variationist perspective on the choices made in the linguistic system. To combine the different models, we ...

We examine different ensemble methods, including an oracle, to estimate the upper-limit of classification accuracy for Native Language Identification (NLI). The oracle outperforms state-of-the-art systems by over 10% and results indicate that for many misclassified texts the correct class label receives a significant portion of the ensemble votes, often being the runner-up. We also present a pi...