Sistemas granulares evolutivos

Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. Métodos e algoritmos têm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princípio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação é uma técnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparência, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho são considerados dados numéricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares é baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem é particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de séries temporais e controle usando dados sintéticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados não-estacionários com mudanças graduais e abruptas de regime é também analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saída. As abordagens e o paradigma introduzidos constituem uma extensão natural de sistemas inteligentes evolutivos para processamento de dados numéricos a sistemas granulares evolutivos para processamento de dados granularesAbstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streamsDoutoradoAutomaçãoDoutor em Engenharia Elétric

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control

An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.</jats:p

Development of a Crisp Fuzzy-Like Controller Using Formula-Based and Vectorized Approaches

Simplifying of implementation of linear state feedback fuzzy controllers is investigated through the thesis. One of the most important problems in fuzzy controller design is the number of fuzzy subsets (membership functions) for each fuzzy input/output variable. The number of fuzzy subsets and consequently the number of fuzzy rules should be big enough to achieve good approximation of control surface and have a smooth and robust control. However as the number of rules increases, the memory space, and program cycle time and total project cost will also increase dramatically. The thesis proposes crisp-fuzzy like controller derived by two novel approaches. The first one which is formula based crisp fuzzy-like controller proves that the monotonic fuzzy controller is similar to nonlinear saturated controller and then represents several different controller formulas. The second controller namely vectorized crisp fuzzy -like controller maps the fuzzy variables in a vectorial space and derives formula that has the structure similar to PID controllers. The proposed controllers are inspired from fuzzy logic where they can express the control law semantically but they are absolutely crips.Consenquently the needed memory space is minimizes since the rule table has been replace with the formula. On the other fuzzy controllers have high computational complexity while the new controllers are very simple to design, tune and implment.some new performance indexes also are porposed to evaluate the performance and stability of different controllers. Several well-known industrial models are used for simulation and a dimmer circuit to control the bulb temperature,has been used as a case study. Both simulation and experimental results show that the crips - fuzzy like controllers have the same or in some cases better performance and stability compare with the conventional fuzzy logic controllers, with extra merits of lower memory space and cycle time

Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Comparison of MPPT Systems in Error Optimization using PID, Fuzzy and Hybrid Fuzzy in Multivariable Environment

Recent surveys conducted in the field of Power Control and Engineering show that photovoltaic (PV) systems are currently being discussed worldwide and research on the same is being carried globally. It is necessary to optimize the expanding use of photovoltaic systems through error detection in Maximum Power Point Tracking (MPPT) systems. Through this paper, an attempt is made to develop an efficient photovoltaic MPPT system using hybrid fuzzy technique to extract maximum power under a multivariable environment (changing temperature and irradiance). The MPPT system using Hybrid Controller (combining PID & FLC) has an increased efficiency and optimized output in comparison to the MPPT system using PID and Fuzzy individually. The system has explored a concept of computing academic performance indices with three MPPT models for future research based on global MPP calculation. Citation: Sharma, C., and Jain, A. (2018). Comparison of MPPT Systems in Error Optimization using PID, Fuzzy and Hybrid Fuzzy in Multivariable Environment. Trends in Renewable Energy, 4, 8-21. DOI: 10.17737/tre.2018.4.3.004

Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges

As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to author