fuzzy control systems have had various applications in a wide range of science and engineering in recent years. since an unstable control system is typically useless and potentially dangerous, stability is the most important requirement for any control system (including fuzzy control system). conceptually, there are two types of stability for control systems: lyapunov stability (a special case ...

| A formalism for coding fuzzy models of dynam-ical systems is presented. It is shown that the formalism is rich enough to capture the performance of arbitrary conventional discrete time dynamical systems, whose transition maps are polynomials with rational coeecients. The proof of this fact provides a constructive algorithm for generating fuzzy models to approximate an arbitrary map on a compa...

A neuro-fuzzy modeling for forecasting the future dynamical behavior in vibration testing during satellite qualification is proposed in this paper. Vibration testing is employed for emulating vibrations present during the lifetime launching. There are different levels of excitation during vibration testing in order to verify and assure that the satellite and their sub-systems will support the e...

This paper introduces an evolutionary optimization algorithm taking advantage of multiple populations and an adaptive aging parameter to achieve faster and more robust convergence. As challenging test cases, the evolutionary algorithm is used to optimize parameters for dynamical fuzzy systems. Our results show that the proposed algorithm is capable of outperforming the traditional reference alg...

The problem of disturbance rejection in the control of nonlinear systems with additive disturbance generated by some unforced nonlinear systems, was formulated and solved by {itshape Mukhopadhyay} and {itshape Narendra}, they applied the idea of increasing the order of the system, using neural networks the model of multilayer perceptron on several systems of varying complexity, so the objective...

Abstract: This paper determines the designing of a non-fragile H∞ output feedback controller for a class of nonlinear uncertain dynamical systems with multiple timescales described by a Takagi-Sugeno (TS) fuzzy model. Based on a linear matrix inequality (LMI) approach, we develop a non-fragile H∞ output feedback controller which guarantees the L2-gain of the mapping from the exogenous input noi...

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF Learning Classifier System. In particular, asynchronous Random Boolean Networks are used to represent the traditional c...

In this paper, we introduce the concept of dynamical distance on a nuclear conguration space. We partition the nuclear conguration space into disjoint classes. This classification coincides with the classical partitioning of molecular systems via the concept of conjugacy of dynamical systems. It gives a quantitative criterion to distinguish dierent molecular structures.

Bridging the two seemingly unrelated concepts, fuzzy logic and nonlinear piecewise-smooth dynamical systems theory is chiefly motivated by the concept of soft computing (SC), initiated by Lotfi A. Zadeh, the founder of fuzzy set theory. The principal components of SC, as defined in his initiative for soft computing1, are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (...

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched...