Identificação de uma planta de corrente de um motor de indução utilizando redes de base radial

The present work describes the use of a mathematical tool to solve problems arising from control theory, including the identification, analysis of the phase portrait and stability, as well as the temporal evolution of the plant s current induction motor. The system identification is an area of mathematical modeling that has as its objective the study of techniques which can determine a dynamic model in representing a real system. The tool used in the identification and analysis of nonlinear dynamical system is the Radial Basis Function (RBF). The process or plant that is used has a mathematical model unknown, but belongs to a particular class that contains an internal dynamics that can be modeled.Will be presented as contributions to the analysis of asymptotic stability of the RBF. The identification using radial basis function is demonstrated through computer simulations from a real data set obtained from the plant

Detecção e diagnóstico de falhas não-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo

In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison

Algoritmo papílio como método de proteção de templates para aumentar a segurança em sistemas de identificação biométricos

There are authentication models which use passwords, keys, personal identifiers (cards, tags etc) to authenticate a particular user in the authentication/identification process. However, there are other systems that can use biometric data, such as signature, fingerprint, voice, etc., to authenticate an individual in a system. In another hand, the storage of biometric can bring some risks such as consistency and protection problems for these data. According to this problem, it is necessary to protect these biometric databases to ensure the integrity and reliability of the system. In this case, there are models for security/authentication biometric identification, for example, models and Fuzzy Vault and Fuzzy Commitment systems. Currently, these models are mostly used in the cases for protection of biometric data, but they have fragile elements in the protection process. Therefore, increasing the level of security of these methods through changes in the structure, or even by inserting new layers of protection is one of the goals of this thesis. In other words, this work proposes the simultaneous use of encryption (Encryption Algorithm Papilio) with protection models templates (Fuzzy Vault and Fuzzy Commitment) in identification systems based on biometric. The objective of this work is to improve two aspects in Biometric systems: safety and accuracy. Furthermore, it is necessary to maintain a reasonable level of efficiency of this data through the use of more elaborate classification structures, known as committees. Therefore, we intend to propose a model of a safer biometric identification systems for identification.