Sobre revoluções científicas na matemática

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudo das propriedades dinâmicas e sincronização no mapa de rede de Zaslavsky

Synchronization in nonlinear dynamical systems, especially in chaotic systems, is field of research in several areas of knowledge, such as Mechanical Engineering and Electrical Engineering, Biology, Physics, among others. In simple terms, two systems are synchronized if after a certain time, they have similar behavior or occurring at the same time. The sound and image in a film is an example of this phenomenon in our daily lives. The studies of synchronization include studies of continuous dynamic systems, governed by differential equations or studies of discrete time dynamical systems, also called maps. Maps correspond, in general, discretizations of differential equations and are widely used to model physical systems, mainly due to its ease of computational. It is enough to make iterations from given initial conditions for knowing the trajectories of system. This completion of course work based on the study of the map called ”Zaslavksy Web Map”. The Zaslavksy Web Map is a result of the combination of the movements of a particle in a constant magnetic field and a wave electrostatic propagating perpendicular to the magnetic field. Apart from interest in the particularities of this map, there was objective the deepening of concepts of nonlinear dynamics, as equilibrium points, linear stability, stability non-linear, bifurcation and chaos

Caracterização experimental de sistemas mecânicos com comportamento não-linear

Pós-graduação em Engenharia Mecânica - FEIS

Sobre o uso de sistemas de computação algébrica na solução de problemas de mecânica das estruturas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Preventing chaotic motion in tapping-mode atomic force microscope

During the last 30 years the Atomic Force Microscopy became the most powerful tool for surface probing in atomic scale. The Tapping-Mode Atomic Force Microscope is used to generate high quality accurate images of the samples surface. However, in this mode of operation the microcantilever frequently presents chaotic motion due to the nonlinear characteristics of the tip-sample forces interactions, degrading the image quality. This kind of irregular motion must be avoided by the control system. In this work, the tip-sample interaction is modelled considering the Lennard-Jones potentials and the two-term Galerkin aproximation. Additionally, the State Dependent Ricatti Equation and Time-Delayed Feedback Control techniques are used in order to force the Tapping-Mode Atomic Force Microscope system motion to a periodic orbit, preventing the microcantilever chaotic motion

Lorentz-violating dilatations in momentum space and some extensions on nonlinear actions of Lorentz-algebra-preserving systems

We work on some general extensions of the formalism for theories which preserve the relativity of inertial frames with a nonlinear action of the Lorentz transformations on momentum space. Relativistic particle models invariant under the corresponding deformed symmetries are presented with particular emphasis on deformed dilatation transformations. The algebraic transformations relating the deformed symmetries with the usual (undeformed) ones are provided in order to preserve the Lorentz algebra. Two distinct cases are considered: a deformed dilatation transformation with a spacelike preferred direction and a very special relativity embedding with a lightlike preferred direction. In both analysis we consider the possibility of introducing quantum deformations of the corresponding symmetries such that the spacetime coordinates can be reconstructed and the particular form of the real space-momentum commutator remains covariant. Eventually feasible experiments, for which the nonlinear Lorentz dilatation effects here pointed out may be detectable, are suggested.

A novel approach based on recurrent neural networks applied to nonlinear systems optimization

This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.

Stability and convergence analysis of a neural model applied in nonlinear systems optimization

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are completed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Implementation of two-stage Hopfield model and its application in nonlinear systems

This paper presents an efficient neural network for solving constrained nonlinear optimization problems. More specifically, a two-stage neural network architecture is developed and its internal parameters are computed using the valid-subspace technique. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty or weighting parameters for its initialization.

On energy transfer between vibrating systems under linear and nonlinear interactions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

On energy transfer phenomena, in a nonlinear ideal and nonideal essential vibrating systems, coupled to a (MR) magneto-rheological damper

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An Optimal Linear Control Design for Nonlinear Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)