Speed Neuro-fuzzy Estimator Applied To Sensorless Induction Motor Control

This work proposes the development of an Adaptive Neuro-fuzzy Inference System (ANFIS) estimator applied to speed control in a three-phase induction motor sensorless drive. Usually, ANFIS is used to replace the traditional PI controller in induction motor drives. The evaluation of the estimation capability of the ANFIS in a sensorless drive is one of the contributions of this work. The ANFIS speed estimator is validated in a magnetizing flux oriented control scheme, consisting in one more contribution. As an open-loop estimator, it is applied to moderate performance drives and it is not the proposal of this work to solve the low and zero speed estimation problems. Simulations to evaluate the performance of the estimator considering the vector drive system were done from the Matlab/Simulink(R) software. To determine the benefits of the proposed model, a practical system was implemented using a voltage source inverter (VSI) to drive the motor and the vector control including the ANFIS estimator, which is carried out by the Real Time Toolbox from Matlab/Simulink(R) software and a data acquisition card from National Instruments.

Towards a fuzzy-based multi-classifier selection module for activity recognition applications

Performing activity recognition using the information provided by the different sensors embedded in a smartphone face limitations due to the capabilities of those devices when the computations are carried out in the terminal. In this work a fuzzy inference module is implemented in order to decide which classifier is the most appropriate to be used at a specific moment regarding the application requirements and the device context characterized by its battery level, available memory and CPU load. The set of classifiers that is considered is composed of Decision Tables and Trees that have been trained using different number of sensors and features. In addition, some classifiers perform activity recognition regardless of the on-body device position and others rely on the previous recognition of that position to use a classifier that is trained with measurements gathered with the mobile placed on that specific position. The modules implemented show that an evaluation of the classifiers allows sorting them so the fuzzy inference module can choose periodically the one that best suits the device context and application requirements.

The Fuzzy-Neuro Classifier for Decision Support

This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire process and analyze various types of knowledge. The proposed environment integrates techniques of knowledge and decision process modeling such as neural networks and fuzzy logic-based reasoning methods. The problem of an identification of complex processes with the use of neuro-fuzzy systems is solved. The proposed classifier has been successfully applied for building one decision support systems for solving managerial problem.

The Cascade Neo-Fuzzy Architecture and its Online Learning Algorithm

In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.

Técnica para segmentação automática de imagens microscópicas de componentes sanguíneos e classificação diferencial de leucócitos baseada em lógica fuzzy

Automatic detection of blood components is an important topic in the field of hematology. The segmentation is an important stage because it allows components to be grouped into common areas and processed separately and leukocyte differential classification enables them to be analyzed separately. With the auto-segmentation and differential classification, this work is contributing to the analysis process of blood components by providing tools that reduce the manual labor and increasing its accuracy and efficiency. Using techniques of digital image processing associated with a generic and automatic fuzzy approach, this work proposes two Fuzzy Inference Systems, defined as I and II, for autosegmentation of blood components and leukocyte differential classification, respectively, in microscopic images smears. Using the Fuzzy Inference System I, the proposed technique performs the segmentation of the image in four regions: the leukocyte’s nucleus and cytoplasm, erythrocyte and plasma area and using the Fuzzy Inference System II and the segmented leukocyte (nucleus and cytoplasm) classify them differentially in five types: basophils, eosinophils, lymphocytes, monocytes and neutrophils. Were used for testing 530 images containing microscopic samples of blood smears with different methods. The images were processed and its accuracy indices and Gold Standards were calculated and compared with the manual results and other results found at literature for the same problems. Regarding segmentation, a technique developed showed percentages of accuracy of 97.31% for leukocytes, 95.39% to erythrocytes and 95.06% for blood plasma. As for the differential classification, the percentage varied between 92.98% and 98.39% for the different leukocyte types. In addition to promoting auto-segmentation and differential classification, the proposed technique also contributes to the definition of new descriptors and the construction of an image database using various processes hematological staining

Adaptação do paradigma orientado a notificações para desenvolvimento de sistemas fuzzy

This work proposes to adjust the Notification Oriented Paradigm (NOP) so that it provides support to fuzzy concepts. NOP is inspired by elements of imperative and declarative paradigms, seeking to solve some of the drawbacks of both. By decomposing an application into a network of smaller computational entities that are executed only when necessary, NOP eliminates the need to perform unnecessary computations and helps to achieve better logical-causal uncoupling, facilitating code reuse and application distribution over multiple processors or machines. In addition, NOP allows to express the logical-causal knowledge at a high level of abstraction, through rules in IF-THEN format. Fuzzy systems, in turn, perform logical inferences on causal knowledge bases (IF-THEN rules) that can deal with problems involving uncertainty. Since PON uses IF-THEN rules in an alternative way, reducing redundant evaluations and providing better decoupling, this research has been carried out to identify, propose and evaluate the necessary changes to be made on NOP allowing to be used in the development of fuzzy systems. After that, two fully usable materializations were created: a C++ framework, and a complete programming language (LingPONFuzzy) that provide support to fuzzy inference systems. From there study cases have been created and several tests cases were conducted, in order to validate the proposed solution. The test results have shown a significant reduction in the number of rules evaluated in comparison to a fuzzy system developed using conventional tools (frameworks), which could represent an improvement in performance of the applications.

REAL TIME FUZZY CONTROLLER FOR QUADROTOR STABILITY CONTROL

In this report, we develop an intelligent adaptive neuro-fuzzy controller by using adaptive neuro fuzzy inference system (ANFIS) techniques. We begin by starting with a standard proportional-derivative (PD) controller and use the PD controller data to train the ANFIS system to develop a fuzzy controller. We then propose and validate a method to implement this control strategy on commercial off-the-shelf (COTS) hardware. An analysis is made into the choice of filters for attitude estimation. These choices are limited by the complexity of the filter and the computing ability and memory constraints of the micro-controller. Simplified Kalman filters are found to be good at estimation of attitude given the above constraints. Using model based design techniques, the models are implemented on an embedded system. This enables the deployment of fuzzy controllers on enthusiast-grade controllers. We evaluate the feasibility of the proposed control strategy in a model-in-the-loop simulation. We then propose a rapid prototyping strategy, allowing us to deploy these control algorithms on a system consisting of a combination of an ARM-based microcontroller and two Arduino-based controllers. We then use a combination of the code generation capabilities within MATLAB/Simulink in combination with multiple open-source projects in order to deploy code to an ARM CortexM4 based controller board. We also evaluate this strategy on an ARM-A8 based board, and a much less powerful Arduino based flight controller. We conclude by proving the feasibility of fuzzy controllers on Commercial-off the shelf (COTS) hardware, we also point out the limitations in the current hardware and make suggestions for hardware that we think would be better suited for memory heavy controllers.

A rule-based hybrid method for anomaly detection in online-social-network graphs

Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Utilización de la lógica borrosa en la selección de personas e ideas para la participación en programas públicos de ayuda a la creación de empresas

[ES] Se propone en este trabajo un modelo de control borroso que ayude a filtrar y seleccionar las solicitudes de subvención que pueda recibir una institución pública en un programa de fomento para la creación y desarrollo de nuevas iniciativas empresariales. Creemos que la utilización de la lógica borrosa presenta ventajas sobre los procedimientos ordinarios ya que nos movemos en un escenario de actuación complejo y vago. El control borroso introduce el conocimiento de los expertos de un modo muy natural mediante variables lingüísticas y procesos de inferencia propios del lenguaje ordinario, lo que facilita la toma de decisiones en situaciones complejas. Nuestro modelo considera por un lado la idea empresarial y por otro la persona . Los indicadores y criterios que los expertos consideran relevantes para la evaluación de la subvención son modelados mediante variables lingüísticas y tratados como antecedentes y consecuentes de un motor de inferencia borroso, cuya salida nos proporciona la valoración final de la solicitud. Al final de nuestro trabajo resolvemos un caso práctico sencillo para aclarar el procedimiento.

Aplicação de Lógica Nebulosa para Previsão do Risco de Escorregamento de Taludes em Solo Residual.

A estabilidade de taludes naturais é um tema de grande interesse ao engenheiro geotécnico, face às significativas perdas econômicas, e até mesmo humanas, resultantes da ruptura de taludes. Estima-se que a deflagração de escorregamentos já provocou milhares de mortes, e dezenas de bilhões de dólares em prejuízos anuais em todo o mundo. Os fenômenos de instabilização de encostas são condicionados por muitos fatores, como o clima, a litologia e as estruturas das rochas, a morfologia, a ação antrópica e outros. A análise dos condicionantes geológicos e geotécnicos de escorregamentos proporciona a apreciação de cada um dos fatores envolvidos nos processos de instabilização de encostas, permitindo a obtenção de resultados de interesse, no que diz respeito ao modo de atuação destes fatores. O presente trabalho tem como objetivo a utilização da Lógica Nebulosa (Fuzzy) para criação de um Modelo que, de forma qualitativa, forneça uma previsão do risco de escorregamento de taludes em solos residuais. Para o cumprimento deste objetivo, foram estudados os fatores envolvidos nos processos de instabilização de encostas, e a forma como estes fatores se interrelacionam. Como experiência do especialista para a elaboração do modelo, foi analisado um extenso banco de dados de escorregamentos na cidade do Rio de Janeiro, disponibilizado pela Fundação Geo-Rio. Apresenta-se, neste trabalho, um caso histórico bem documentado para a validação do Modelo Fuzzy e análises paramétricas, realizadas com o objetivo verificar a coerência do modelo e a influência de cada um dos fatores adotados na previsão do risco de escorregamento. Dentre as principais conclusões, destaca-se a potencialidade da lógica nebulosa na previsão de risco de escorregamentos de taludes em solo residual, aparecendo como uma ferramenta capaz de auxiliar na detecção de áreas de risco.

Lógica nebulosa aplicada a um sistema de detecção de intrusos em computação em nuvem.

O objetivo deste trabalho é avaliar os riscos de ocorrências de intrusos em um sistema de computação em nuvem para sistemas distribuídos utilizando lógica nebulosa. A computação em nuvem é um tema que vem sendo bastante abordado e vem alavancando discussões calorosas, tanto na comunidade acadêmica quanto em palestras profissionais. Embora essa tecnologia esteja ganhando mercado, alguns estudiosos encontram-se céticos afirmando que ainda é cedo para se tirar conclusões. Isto se deve principalmente por causa de um fator crítico, que é a segurança dos dados armazenados na nuvem. Para esta dissertação, foi elaborado um sistema distribuído escrito em Java com a finalidade de controlar um processo de desenvolvimento colaborativo de software na nuvem, o qual serviu de estudo de caso para avaliar a abordagem de detecção de intrusos proposta. Este ambiente foi construído com cinco máquinas (sendo quatro máquinas virtuais e uma máquina real). Foram criados dois sistemas de inferência nebulosos, para análise de problemas na rede de segurança implementados em Java, no ambiente distribuído. Foram realizados diversos testes com o intuito de verificar o funcionamento da aplicação, apresentando um resultado satisfatório dentro dessa metodologia.

Uma técnica híbrida para geração de rotas em espaço geográfico com obstáculos.

Este trabalho está inserido no campo da Geomática e se concentra, mais especificamente, no estudo de métodos para exploração e seleção de rotas em espaços geográficos sem delimitação prévia de vias trafegáveis. As atividades que poderiam se beneficiar de estudos desse tipo estão inseridas em áreas da engenharia, logística e robótica. Buscou-se, com as pesquisas realizadas nesse trabalho, elaborar um modelo computacional capaz de consultar as informações de um terreno, explorar uma grande quantidade de rotas viáveis e selecionar aquelas rotas que oferecessem as melhores condições de trajetória entre dois pontos de um mapa. Foi construído um sistema a partir do modelo computacional proposto para validar sua eficiência e aplicabilidade em diferentes casos de estudo. Para que esse sistema fosse construído, foram combinados conceitos de sistemas baseados em agentes, lógica nebulosa e planejamento de rotas em robótica. As informações de um terreno foram organizadas, consumidas e apresentadas pelo sistema criado, utilizando mapas digitais. Todas as funcionalidades do sistema foram construídas por meio de software livre. Como resultado, esse trabalho de pesquisa disponibiliza um sistema eficiente para o estudo, o planejamento ou a simulação de rotas sobre mapas digitais, a partir de um módulo de inferência nebuloso aplicado à classificação de rotas e um módulo de exploração de rotas baseado em agentes autônomos. A perspectiva para futuras aplicações utilizando o modelo computacional apresentado nesse trabalho é bastante abrangente. Acredita-se que, a partir dos resultados alcançados, esse sistema possa ajudar a reduzir custos e automatizar equipamentos em diversas atividades humanas.

最优Fuzzy—GA PID控制器及其应用

提出了一种基于模糊推理与遗传算法的最优PID控制器的设计方法，该控制器由离线和在线2部分组成，在离线部分，以系统响应的超调量、上升时间及调速时间为性能指标，利用遗传算法搜索出一组最优的PID参数Kp^*，Ti^*及Td^*，为在线部分调节的初始值，在在线部分，采用一个专用的PID参数优化程序，以离线部分获得的Kp^*,Ti^*及Td^*为基础，根据系统当前的误差e和误差变化率·↑e，通过模糊推理在线调整系统瞬态响应的PID参数，以确保系统的响应具有最优的动态和稳态性能，计算机仿真结果表明，与传统的PID控制器相比，这种最优PID控制器具有良好的控制性能和鲁棒性能，可用于控制不同的对象和过程。

基于模糊推理水下作业系统运动控制研究

水下作业系统是运动学冗余系统,本文将模糊推理方法融入基于任务优先运动学控制算法,对系统载体与机械手进行协调运动分配,同时对系统多个任务进行优化。通过带有3自由度水下机械手的水下作业系统进行算例仿真研究,说明运动控制算法的有效性。