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In many practical applications, it is well known that data collected inevitably contain one or more anomalous outliers; that is, observations that are well separated from the majority or bulk of the data, or in some fashion deviate from the general pattern of the data. The occurrence of outliers may be due to misplaced decimal points, recording errors, transmission errors, or equipment failure. These outliers can lead to erroneous parameter estimation and consequently affect the correctness and accuracy of the model inference. In order to solve these problems, three robust fuzzy neural networks (FNNs) will be proposed in this dissertation. This provides alternative learning machines when faced with general nonlinear learning problems. Our emphasis will be put particularly on the robustness of these learning machines against outliers. Though we consider only FNNs in this study, the extension of our approach to other neural networks, such as artificial neural networks and radial basis function networks, is straightforward. In the first part of the dissertation, M-estimators, where M stands for maximum likelihood, frequently used in robust regression for linear parametric regression problems will be generalized to nonparametric Maximum Likelihood Fuzzy Neural Networks (MFNNs) for nonlinear regression problems. Simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) will be derived. In the second part of the dissertation, least trimmed squares estimators, abbreviated as LTS-estimators, frequently used in robust (or resistant) regression for linear parametric regression problems will be generalized to nonparametric least trimmed squares fuzzy neural networks, abbreviated as LTS-FNNs, for nonlinear regression problems. Again, simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) algorithms will be provided. In the last part of the dissertation, by combining the easy interpretability of the parametric models and the flexibility of the nonparametric models, semiparametric fuzzy neural networks (semiparametric FNNs) and semiparametric Wilcoxon fuzzy neural networks (semiparametric WFNNs) will be proposed. The corresponding learning rules are based on the backfitting procedure which is frequently used in semiparametric regression.

In this thesis, M-estimators with Ramsay¡¦s function used in robust regression theory for linear parametric regression problems will be generalized to nonparametric Ramsay fuzzy neural networks (RFNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on incremental gradient descent and iteratively reweighted least squares (IRLS) will be derived. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed RFNNs. Simulation results show that the RFNNs proposed in this thesis have good robustness against outliers.

In this thesis, an architecture for the adaptive mode transition control of unmanned aerial vehicles (UAV) is presented. The proposed architecture consists of three levels: the highest level is occupied by mission planning routines where information about way points the vehicle must follow is processed. The middle level uses a trajectory generation component to coordinate the task execution and provides set points for low-level stabilizing controllers. The adaptive mode transitioning control algorithm resides at the lowest level of the hierarchy consisting of a mode transitioning controller and the accompanying adaptation mechanism. The mode transition controller is composed of a mode transition manager, a set of local controllers, a set of active control models, a set point filter, a state filter, an automatic trimming mechanism and a dynamic compensation filter. Local controllers operate in local modes and active control models operate in transitions between two local modes. The mode transition manager determines the actual mode of operation of the vehicle based on a set of mode membership functions and activates a local controller or an active control model accordingly. The adaptation mechanism uses an indirect adaptive control methodology to adapt the active control models. For this purpose, a set of plant models based on fuzzy neural networks is trained based on input/output information from the vehicle and used to compute sensitivity matrices providing the linearized models required by the adaptation algorithms. The effectiveness of the approach is verified through software-in-the-loop simulations, hardware-in-the-loop simulations and flight testing.

Yes / This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments.

[pt] Observando a dificuldade de batimento (match) dos padrões de comportamento das funções de autocorrelação e de autocorrelação parcial teóricas com as respectivas funções e as autocorrelação e de autocorrelação parcial estimadas de uma séries temporal, aliada ao fato da dificuldade em definir um número em específico como delimitador inequívoco do que seja um lag significativo, tornam clara a dose de julgamento subjetivo a ser realizado por um especialista de análise de séries temporais na tomada de decisão sobre a estrutura de Box & Jenkins adequada a ser escolhida para modelar o processo estocástico sendo estudado. A matemática nebulosa permite a criação de sistemas de inferências nebulosas (inferência dedutiva) e representa o conhecimento de forma explícita, através de regras nebulosas, possibilitando, facilmente, o entendimento do sistema em estudo. Por outro lado, um modelo de redes neurais representa o conhecimento de forma implícita, adquirido através de exemplos (dados), possuindo excelente capacidade de generalização (inferência indutiva). Esta tese apresenta um sistema especialista composto de cinco redes neurais nebulosas do tipo retropropagação para o auxílio na análise de séries temporais não sazonais. O sistema indica ao usuário a estrutura mais adequada, dentre as estruturas AR(1), MA (1), AR(2), MA(2) e ARMA(1,1), tomando como base a menor distância Euclidiana entre os valores esperados e as saídas das redes neurais nebulosas. / [en] It is well known the difficulties associated with the tradicional procedure for model identification of the Box & Jenkins model through the pattern matching of the theoretical and estimated ACF and PACF. The decision on the acceptance of the null hypothesis of zero ACF (or PACF) for a given lag is based on a strong asymptotic result, particularly for the PACF, leading, sometimes, to wrong decisions on the identified order of the models. The fuzzy logic allows one to infer system governed by incomplete or fuzzy knowledge (deductive inference) using a staighforward formulation of the problem via fuzzy mathematics. On the other hand, the neural network represent the knowledge in a implicit manner and has a great generalization capacity (inductive inference). In this thesis we built a specialist system composed of 5 fuzzy neural networks to help on the automatic identificationof the following Box & Jenkins ARMA structure AR(1), MA(1), AR(2), MA(2) and ARMA (1,1), through the Euclidian distance between the estimated output of the net and the corresponding patterns of each one of the five structures.

No / Selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task, which requires significant expert knowledge and experience. Also, the application of a control action for solving a local traffic problem could create traffic congestion at different locations in the network because of the strong interrelations between traffic situations at different locations of a road network. Therefore, coordination of control strategies is required to make sure that all available control actions serve the same objective. In this paper, an Intelligent Traffic Control System (ITCS) based on a coordinated-agent approach is proposed to assist the human operator of a road traffic control centre to manage the current traffic state. In the proposed system, the network is divided into sub-networks, each of which has its own associated agent. The agent of the sub-network with an incident reacts with other affected agents in order to select the optimal traffic control action, so that a globally acceptable solution is found. The agent uses an effective way of calculating the control action fitness locally and globally. The capability of the proposed ITCS has been tested for a case study of a part of the traffic network in the Riyadh city of Saudi Arabia. The obtained results show its ability to identify the optimal global control action. (C) 2012 Elsevier B.V. All rights reserved.

Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. A general T2FNN is computational intensive due to the complexity of type 2 to type 1 reduction. Therefore the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates can not be both negative. Further, due to variation of the initial MF parameters, i.e. the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search better-fit spread rate for uncertain means and near optimal learnings for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.

Orientador: Secundino Soares Filho / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-13T03:22:09Z (GMT). No. of bitstreams: 1 Monte_Bruno_M.pdf: 1808851 bytes, checksum: 7268c8ef4bb1363f9876841d3a0ce345 (MD5) Previous issue date: 2009 / Resumo: O planejamento energético de sistemas hidrotérmicos caracteriza-se pela otimização dos recursos hidráulicos através da maximização da operação hidrelétrica e da minimização da operação térmica. Seu objetivo é garantir um atendimento à carga de maneira econômica e confiável durante todo o horizonte de estudo. Este problema pode ser caracterizado como de natureza complexa, dado que suas características o definem como um problema de grande porte, dinâmico, estocástico e não-linear. Não obstante muitas técnicas já terem sido propostas para solução deste problema, não existe, ainda, uma metodologia unânime que aborde todas essas características com eficiência. A Programação Dinâmica, que é uma das técnicas mais populares utilizadas, tem sua aplicação limitada em sistemas reais, dado que exige um elevado esforço computacional. Neste trabalho, foi proposta uma metodologia alternativa para abordagem do planejamento da operação de médio prazo de sistemas hidrotérmicos. A metodologia proposta é baseada em um Sistema de Inferência Neural-Nebulosa Adaptativo atuando em paralelo com um modelo de otimização determinístico com perfeita previsão de vazão. A informação do otimizador determinístico é utilizada no treinamento da rede, que gera uma base de regra de inferência nebulosa que reproduzirá o comportamento ótimo da usina através da definição da vazão turbinada, em cada estágio, em função das varáveis de entrada estipuladas. A performance da metodologia Neural Nebulosa proposta foi comparada com outras modelagens, como a Programação Dinâmica Determinística, a Programação Dinâmica Estocástica e o Controle de Malha Aberta, através de simulações em cinco usinas hidrelétricas do parque gerador brasileiro considerando as vazões afluentes do histórico. Os resultados indicaram que a metodologia Neural Nebulosa proposta apresentou desempenho similar a abordagens mais tradicionais e que se configuram computacionalmente menos eficiente. / Abstract: The long term hydrothermal scheduling lies in the optimization of the water resource usage through the maximization of the hydroelectric production and the minimization of the thermal plants operation. Its goal is to assure an economic and reliable load supply throughout the study stages. This problem can be characterized by exhibiting a complex nature, since its characteristics define it as a large scale, dynamic, stochastic and nonlinear problem. Although many optimization approaches have already been proposed to answer the hydrothermal scheduling problem, until now, there is no unanimous approach that is able to cope efficiently with all the problem issues. Dynamic Programming, which is one of the most commonly used techniques to deal with this problem, is limited regarding its application on real systems since its computational requirements tend to be heavy. In this work we proposed an alternative approach to deal with the long term hydrothermal scheduling. The proposed technique is based on an Adaptive Neuro-Fuzzy Inference System working in parallel with a deterministic optimization model with perfect inflows forecasting. The optimal operation information is processed by the network that produces fuzzy rules describing the optimal decisions of the plant through the definition of the amount of discharge in each stage and depending on the chosen input variables. The performance of the proposed Neuro-Fuzzy approach was compared to other policies, including Deterministic Dynamic Programming, Stochastic Dynamic Programming and Open- Loop Feedback Control, by simulation using historical inflow records of five different Brazilian hydroelectric power plants. The results demonstrated that the Neuro-Fuzzy approach provided similar and competitive performance to less computationally efficient and commonly used policies. / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica