Solution of Dual Fuzzy Equations Using a New Iterative Method

In this paper, a new hybrid scheme based on learning algorithm of fuzzy neural network (FNN) is offered in order to extract the approximate solution of fully fuzzy dual polynomials (FFDPs). Our FNN in this paper is a five-layer feed-back FNN with the identity activation function. The input-output relation of each unit is defined by the extension principle of Zadeh. The output from this neural network, which is also a fuzzy number, is numerically compared with the target output. The comparison of the feed-back FNN method with the feed-forward FNN method shows that the less error is observed in the feed-back FNN method. An example based on applications are given to illustrate the concepts, which are discussed in this paper

Nonabelian Phenomena on D-branes

A remarkable feature of D-branes is the appearance of a nonabelian gauge theory in the description of several (nearly) coincident branes. This nonabelian structure plays an important role in realizing various geometric effects with D-branes. In particular, the branes' transverse displacements are described by matrix-valued scalar fields and so noncommutative geometry naturally appears in this framework. I review the action governing this nonabelian theory, as well as various related physical phenomena such as the dielectric effect, giant gravitons and fuzzy funnels.Comment: Lecture at Leuven workshop on ``The quantum structure of spacetime and the geometrical nature of fundamental interactions'' (September 13-19, 2002); ref.'s adde

Modeling and Control of Uncertain Nonlinear Systems

A survey of the methodologies associated with the modeling and control of uncertain nonlinear systems has been given due importance in this paper. The basic criteria that highlights the work is relied on the various patterns of techniques incorporated for the solutions of fuzzy equations that corresponds to fuzzy controllability subject. The solutions which are generated by these equations are considered to be the controllers. Currently, numerical techniques have come out as superior techniques in order to solve these types of problems. The implementation of neural networks technique is contributed in the complex way of dealing the appropriate coefficients and solutions of the fuzzy systems

ON QUASI NEWTON METHOD FOR SOLVING FUZZY NONLINEAR EQUATIONS

This paper presents Quasi Newton’s (QN) approach for solving fuzzy nonlinear equations. The method considers an approximation of the Jacobian matrix which is updated as the iteration progresses. Numerical illustrations are carried, and the results shows that the proposed method is very encouraging