Rede neural com dinâmica interna aplicada a problemas de identificação e controle não-linear /

Tese (Doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico

A Neurogenetic Algorithm Based on Rational Agents

Lately, a lot of research has been conducted on the automatic design of artificial neural networks (ADANNs) using evolutionary algorithms, in the so-called neuro-evolutive algorithms (NEAs). Many of the presented proposals are not biologically inspired and are not able to generate modular, hierarchical and recurrent neural structures, such as those often found in living beings capable of solving intricate survival problems. Bearing in mind the idea that a nervous system's design and organization is a constructive process carried out by genetic information encoded in DNA, this paper proposes a biologically inspired NEA that evolves ANNs using these ideas as computational design techniques. In order to do this, we propose a Lindenmayer System with memory that implements the principles of organization, modularity, repetition (multiple use of the same sub-structure), hierarchy (recursive composition of sub-structures), minimizing the scalability problem of other methods. In our method, the basic neural codification is integrated to a genetic algorithm (GA) that implements the constructive approach found in the evolutionary process, making it closest to biological processes. Thus, the proposed method is a decision-making (DM) process, the fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. In other words, the penalty approach implemented through the fitness function automatically rewards the economical ANNs with stronger generalization and extrapolation capacities. Our method was initially tested on a simple, but non-trivial, XOR problem. We also submit our method to two other problems of increasing complexity: time series prediction that represents consumer price index and prediction of the effect of a new drug on breast cancer. In most cases, our NEA outperformed the other methods, delivering the most accurate classification. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems

Applying Intelligent Multi-Agents to Reduce False Alarms in Wind Turbine Monitoring Systems

This study proposes a method for improving the capability of a data-driven multi-agent system (MAS) to perform condition monitoring and fault detection in industrial processes. To mitigate the false fault-detection alarms, a co-operation strategy among software agents is proposed because it performs better than the individual agents. Few steps transform this method into a valuable procedure for improving diagnostic certainty. First, a failure mode and effects analysis are performed to select physical monitoring signals of the industrial process that allow agents to collaborate via shared signals. Next, several artificial neural network (ANN) models are generated based on the normal behavior operation conditions of various industrial subsystems equipped with monitoring sensors. Thereafter, the agents use the ANN-based expected behavior models to prevent false alarms by continuously monitoring the measurement samples of physical signals that deviate from normal behavior. Finally, this method is applied to a wind turbine. The system and tests use actual data from a wind farm in Spain. The results show that the collaboration among agents facilitates the effective detection of faults and can significantly reduce false alarms, indicating a notable advancement in the industrial maintenance and monitoring strategy

Applying Intelligent Multi-Agents to Reduce False Alarms in Wind Turbine Monitoring Systems

This study proposes a method for improving the capability of a data-driven multi-agent system (MAS) to perform condition monitoring and fault detection in industrial processes. To mitigate the false fault-detection alarms, a co-operation strategy among software agents is proposed because it performs better than the individual agents. Few steps transform this method into a valuable procedure for improving diagnostic certainty. First, a failure mode and effects analysis are performed to select physical monitoring signals of the industrial process that allow agents to collaborate via shared signals. Next, several artificial neural network (ANN) models are generated based on the normal behavior operation conditions of various industrial subsystems equipped with monitoring sensors. Thereafter, the agents use the ANN-based expected behavior models to prevent false alarms by continuously monitoring the measurement samples of physical signals that deviate from normal behavior. Finally, this method is applied to a wind turbine. The system and tests use actual data from a wind farm in Spain. The results show that the collaboration among agents facilitates the effective detection of faults and can significantly reduce false alarms, indicating a notable advancement in the industrial maintenance and monitoring strategy

Rocket tracking impact point prediction using α-β, standard Kalman, extended, Kalman, and unscented Kalman filters: a comparative analysis

Accurate information about the impact point (IP) of a suborbital rocket on Earth’s surface during a launch is an important requirement for range safety operations. Four different estimators, i.e., the α-β filter, standard Kalman filter (SKF), extended Kalman filter (EKF), and unscented Kalman filter (UKF), are considered for the suborbital rocket tracking problem, whose data are used specifically for improving the accuracy of the IP prediction (IPP) of these vehicles. This paper presents a comparative analysis between the results of the estimators. Rocket flight data are discussed to demonstrate the advantages and disadvantages of the estimators and to determine the inherent limitations in predicting the aerodynamic effects found in certain flight situations. We discuss the appropriate mathematical model of a filter capable of running the real-time algorithm for the estimation of target position and velocity. This work uses actual data from a radar sensor to evaluate the tracking algorithms. We insert the filter result into the mathematical model developed to predict the rocket IP on Earth's surface. The main goal of this study is to evaluate the performance of four different estimators when specifically applied for the improvement of the IPP of suborbital rockets. It is demonstrated that the UKF outperforms all other tracking algorithms in terms of the accuracy and robustness of IP estimation.Informações precisas sobre o ponto de impacto (PI) de um foguete suborbital na superfície da Terra durante um lançamento são requisitos importantes para operações de segurança dos sítos de lançamento. Quatro estimadores diferentes, como filtro α-β, filtro Kalman padrão (FKP), filtro Kalman estendido (FKE) e filtro Kalman sem cheiro (FKU), são considerados para o problema de rastreamento suborbital de foguetes, cujos dados são usados especificamente para melhorar a precisão da predição do PI (PPI) desses veículos. Este artigo apresenta uma análise comparativa entre os resultados dos estimadores. Os dados de voo de foguetes são analisados no sentido de demonstrar as vantagens e desvantagens dos estimadores e determinar as limitações inerentes à previsão dos efeitos aerodinâmicos encontrados em determinadas situações de voo. Discutimos o modelo matemático apropriado de um filtro capaz de executar o algoritmo em tempo real para as estimativas da posição e velocidade do alvo. Este trabalho utiliza dados reais de um sensor de radar para avaliar os algoritmos de rastreamento. Inserimos o resultado do filtro no modelo matemático desenvolvido para prever o PI do foguete na superfície da Terra. O principal objetivo deste estudo é avaliar o desempenho de quatro estimadores diferentes, quando aplicados especificamente na melhoria da PPI de foguetes suborbitais. É demonstrado que o FKU supera todos os outros algoritmos de rastreamento em termos de precisão e robustez da estimativa do PI.La información precisa sobre el punto de impacto (PI) de un cohete suborbital en la superficie de la Tierra durante un lanzamiento es un requisito importante para las operaciones de seguridad del sitio de lanzamiento. Se consideran cuatro estimadores diferentes, como el filtro α-β, el filtro Kalman estándar (FKP), el filtro Kalman extendido (FKE) y el filtro Kalman inodoro (FKU) para el problema de seguimiento de cohetes suborbitales, cuyos datos se utilizan específicamente para mejorar la precisión de predicción de PI (PPI) de estos vehículos. Este artículo presenta un análisis comparativo entre los resultados de los estimadores. Los datos del vuelo del cohete se analizan para demostrar las ventajas y desventajas de los estimadores y para determinar las limitaciones inherentes a la predicción de los efectos aerodinámicos encontrados en ciertas situaciones de vuelo. Discutimos el modelo matemático apropiado de un filtro capaz de ejecutar el algoritmo en tiempo real para estimar la posición y la velocidad del objetivo. Este trabajo utiliza datos reales de un sensor de radar para evaluar los algoritmos de seguimiento. Insertamos el resultado del filtro en el modelo matemático desarrollado para predecir el PI del cohete en la superficie de la Tierra. El objetivo principal de este estudio es evaluar el rendimiento de cuatro estimadores diferentes, cuando se aplica específicamente para mejorar el PPI del cohete suborbital. Se muestra que FKU supera a todos los demás algoritmos de seguimiento en términos de precisión y solidez de la estimación de PI