" The problem of learning rules for a fuzzy inference model directly from empirical observations, without resorting to assessments from experts is considered. We develop a method that builds uncertain rules from a set of examples. These rules match the following pattern: If X is A then Y is B is [a,/3], where A and B are fuzzy sets representing fuzzy restrictions on the variables X and Y; a and...

F.X. Albizuri, A.I. Gonzalez, M. Graña, A. d’Anjou University of the Basque Country Informatika Fakultatea, P.K. 649, 20080 Donostia, Spain E-mail: [email protected]; Fax: + 34 943 219306 Abstract. In this paper we define a Boltzmann machine for modelling probability distributions on categorical data, that is, distributions on a set of variables with a finite discrete range. The distribution m...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

In this manuscript, a new method is proposed to provide a perfect tracking of the supercavitation system based on a new two-state model. The tracking of the pitch rate and angle of attack for fin and cavitator input is of the aim. The pitch rate of the supercavitation with respect to fin angle is found as a non-minimum phase behavior. This effect reduces the speed of command pitch rate. Control...

-An iterative learning control scheme is presented for a class of nonlinear dynamic systems which includes holonomic systems as its subset. The control scheme is composed of two types of control methodology: a linear feedback mechanism and a feedforward learning strategy. At each iteration, the linear feedback provides stability of the system and keeps its state errors within uniform bounds. Th...

The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance from training set characteristics. We investigate several variations of this basic scenario, inclu...

To solve trajectory tracking problem of switched system with sensor saturation, an iterative learning control algorithm is proposed. The method uses actual measurement error to modify the variable on premise that rule does not change along iteration axis, but it randomly changes time axis. Moreover, by dealing saturation via diagonal matrix method, convergence strictly proved in sense λ-norm, a...