A new iterative learning controller is proposed for a general unknown discrete time-varying nonlinear non-affine system represented by NARMAX (Nonlinear Autoregressive Moving Average with eXogenous inputs) model. The proposed controller is composed of an iterative learning neural identifier and an iterative learning controller. Iterative learning control and iterative learning identification ar...

This paper introduces the Fast Iterative Rule-based Linguistic Classifier (FaIRLiC), a Genetic Fuzzy Rule-Based Classification System (GFRBCS) which targets at reducing the structural complexity of the resulting rule base, as well as its learning algorithm’s computational requirements, especially when dealing with high-dimensional feature spaces. The proposed methodology follows the principles ...

In standard models of iterative thinking, players choose a fixed rule level from a fixed rule hierarchy. Nonequilibrium behavior emerges when players do not perform enough thinking steps. Existing approaches, however, are inherently static. This paper introduces a Bayesian level-k model, in which players perform Bayesian updating of their beliefs on opponents’ rule levels and best-respond with ...

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain diierent Genetic Fuzzy Rule-Based Systems, i. e., evolutionary algorithm-based processes to automatically design Fuzzy Rule-Based Systems by learning and/or tuning the Fuzzy Rule ...

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the rst one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy...

In this paper we consider a learning rule whose underlying space, possibly infinite dimensional, is equipped with an inner product. The rule proposed is a generalization of Oja’s maximum eigenfilter algorithm. We study its convergence properties and iterative behavior. We observe a whole variety of dynamical behaviors. We establish conditions on parameter values generating chaoticity as well as...

This paper presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabiliz...

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rule-based systems, i.e., evolutionary algorithm-based processes to automatically design fuzzy rulebased systems by learning andror tuning the fuzzy rule b...