Catálogo de publicaciones - libros

The term hybrid dynamic system is a term for a mathematical system that combines behavior of a continuous nature with discontinuous changes. Such systems are often formed by the underlying computational representation of models used in the design of control and signal processing applications, for example in the automotive and aerospace industries. This paper outlines the benefits of Model-Based Design and illustrates how many different formalisms may be essential in model elaboration, such as time-based block diagrams, state transition diagrams, entity-flow networks, and multi-body diagrams. The basic elements of the underlying hybrid dynamic system computational representation are presented and it is shown how these elements combine to form different classes of behaviors that need to be handled for simulation.

This work presents study and experimentation for object recognition when isolated objects are under discussion. The circumstances of similarity transformations, presence of noise, and occlusion have been included as the part of the study. For simplicity, instead of objects, outlines of the objects have been used for the whole process of the recognition. Fourier Descriptors have been used as features of the objects. From the analysis and results using Fourier Descriptors, the following questions arise: What is the optimum number of descriptors to be used? Are these descriptors of equal importance? To answer these questions, the problem of selecting the best descriptors has been formulated as an optimization problem. Particle Swarm Optimization technique has been mapped and used successfully to have an object recognition system using minimal number of Fourier Descriptors. The proposed method assigns, for each of these descriptors, a weighting factor that reflects the relative importance of that descriptor.

In this paper, we design a sterile gesture interface for users, such as doctors/surgeons, to browse medical images in a dynamic medical environment. A vision-based gesture capture system interprets user’s gestures in real-time to navigate through and manipulate an image and data visualization environment. Dynamic navigation gestures are translated to commands based on their relative positions on the screen. The gesture system relies on tracking of the user’s hand based on color-motion cues. A state machine switches from navigation gestures to others such as zoom and rotate. A prototype of the gesture interface was tested in an operating room by neurosurgeons conducting a live operation. Surgeon’s feedback was very positive.

This paper deals with the design and implementation of a software system based on Differential Evolution for the registration of images, and in its testing by means of a set of bidimensional remotely sensed images on two problems, i.e. mosaicking and changes in time. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, without any need for setting control points. A comparison is effected against a publicly available tool, showing the effectiveness of our method.

Clustering is a widely applied tool of data mining to detect the hidden structure of complex multivariate datasets. Hence, clustering solves two kinds of problems simultaneously, it partitions the datasets into cluster of objects that are similar to each other and describes the clusters by cluster prototypes to provide some information about the distribution of the data. In most of the cases these cluster prototypes describe the clusters as simple geometrical objects, like spheres, ellipsoids, lines, linear subspaces etc., and the cluster prototype defines a special distance function. Unfortunately in most of the cases the user does not have prior knowledge about the number of clusters and not even about the proper shape of prototypes. The real distribution of data is generally much more complex than these simple geometrical objects, and the number of clusters depends much more on how well the chosen cluster prototypes fit the distribution of data than on the real groups within the data. This is especially true when the clusters are used for local linear modeling purposes. The aim of this paper is not to define a new distance norm based on a problem dependent cluster prototype but to show how the so called geodesic distance that is based on the exploration of the manifold the data lie on, can be used in the clustering instead of the classical Euclidean distance. The paper presents how this distance measure can be integrated within fuzzy clustering and some examples are presented to demonstrate the advantages of the proposed new methods.

In our paper, the properties of the simplest Takagi-Sugeno (T-S) fuzzy controller are first investigated. Next, based on the well-known Popov criterion with graphical interpretation, a sufficient condition in the frequency domain is proposed to guarantee the globally asymptotical stability of the simplest T-S fuzzy control system. Since this sufficient condition is presented in the frequency do-main, it is of great significance in designing the simplest T-S fuzzy controller in the frequency domain.

B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, differential evolution (DE) ( an evolutionary computation methodology ( can provide a stochastic search to adjust the control points of a BSNN are proposed. DE incorporates an efficient way of self-adapting mutation using small populations. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution and robustness. In this paper, we propose a modified DE using chaotic sequence based on logistic map to train a BSNN. The numerical results presented here indicate that the chaotic DE is effective in building a good BSNN model for nonlinear identification of an experimental nonlinear yo-yo motion control system.

A design for a model-free learning adaptive control (MFLAC) based on pseudo-gradient concepts and optimization procedure by particle swarm optimization (PSO) is presented in this paper. PSO is a method for optimizing hard numerical functions on metaphor of social behavior of flocks of birds and schools of fish. A swarm consists of individuals, called particles, which change their positions over time. Each particle represents a potential solution to the problem. In a PSO system, particles fly around in a multi-dimensional search space. During its flight each particle adjusts its position according to its own experience and the experience of its neighboring particles, making use of the best position encountered by itself and its neighbors. The performance of each particle is measured according to a pre-defined fitness function, which is related to the problem being solved. The PSO has been found to be robust and fast in solving non-linear, non-differentiable, multi-modal problems. Motivation for application of PSO approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with particle swarm optimization for a nonlinear control valve are showed.

B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods that may fall into local minima during the learning procedure. When using feed-forward BSNNs, the quality of approximation depends on the control points (knots) placement of spline functions. This paper describes the application of an artificial immune network inspired optimization method ( the opt-aiNet ( to provide a stochastic search to adjust the control points of a BSNN. The numerical results presented here indicate that artificial immune network optimization methods useful for building a good BSNN model for the nonlinear identification of an experimental nonlinear ball-and-tube system.

We describe in this paper the evolution of modular neural networks using hierarchical genetic algorithms for pattern recognition. Modular Neural Networks (MNN) have shown significant learning improvement over single Neural Networks (NN). For this reason, the use of MNN for pattern recognition is well justified. However, network topology design of MNN is at least an order of magnitude more difficult than for classical NNs. We describe in this paper the use of a Hierarchical Genetic Algorithm (HGA) for optimizing the topology of each of the neural network modules of the MNN. The HGA is clearly needed due to the fact that topology optimization requires that we are able to manage both the layer and node information for each of the MNN modules. Simulation results prove the feasibility and advantages of the proposed approach.