This technical report briefly describes our recent work in the iterative rule learning approach (IRL) of evolutionary learning/genetics-based machine learning. This approach was initiated by the SIA system [12] A more recent example is HIDER [1]. Our approach integrates some of the main characteristics of GAssist [4], a system belonging to the Pittsburgh approach of Evolutionary Learning, into ...

This paper proposes a new alternative to identify and predict intentional human errors based on benefits, costs and deficits (BCD) associated to particular human deviations. It is based on an iterative learning system. Two approaches are proposed. These approaches consist in predicting barrier removal, i.e., non-respect of rules, achieved by human operators and in using the developed iterative ...

In this brief, we propose an observer-based iterative learning control (ILC) scheme for the tracking problem of a class of time-varying nonlinear systems. First, a state observer is derived for the system under consideration, and sufficient conditions for the boundedness and the convergence to zero of the estimation error are given. Thereafter, an iterative learning rule—based on the proposed s...

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

Association classification has been an important type of the rule based classification. A variety of approaches have been proposed to build a classifier based on classification rules. In the prediction stage of the extant approaches, most of the existing association classifiers use the ensemble quality measurement of each rule in a subset rules to predict the class label of the new data. This m...

This research not only dedicated a less restrictive method of iteration-varying function for 8 a learning control law to design a controller but also synchronize two nonlinear systems with free 9 time-delay. In addition, the mathematical theory of system synchronization has proved rigorously 10 and the theory verified through an example to demonstrate the behavior of each parameter in the 11 th...

The performance of a fuzzy k-NN rule depends on the number k and a fuzzy membership-array W[I, mR], where l and m R denote the number of classes and the number of elements in the reference set X R respectively. The proposed learning procedure consists in iterative finding such k and W which minimize the error rate estimated by the 'leaving one out' method.

This paper presents novel Newton algorithms for the blind adaptive decorrelation of real and complex processes. They are globally convergent and exhibit an interesting relationship with the natural gradient algorithm for blind decorrelation and the Goodall learning rule. Indeed, we show that these two later algorithms can be obtained from their Newton decorrelation versions when an exact matrix...

This paper demonstrates that the design of a robust feedback-based Iterative Learning Control (ILC) is straightforward for uncertain linear time invariant (LTI) systems satisfying the robust performance condition. It is shown that once a controller is designed to satisfy the well known robust performance condition, a convergent updating rule involving the performance weighting function can be d...

In this work, we describe SLAVE+R, a fuzzy rule learning system based on SLAVE that includes a reenement module. SLAVE (Structural Learning Algorithm in Vague Environment) was developed for working with noise-aaected systems where the application of some conditions of classical learning theory do not produce good descriptions. This learning system allows to obtain the structure of the rule, i.e...