Implementation and analysis of evolving classifier algorithms in high dimensional space

Orientador: Fernando Antônio Campos GomideDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Sistemas evolutivos e processamento de dados de alta dimensão são de grande importância prática, atualmente sob intensa investigação. Esta dissertação introduz um neuro classificador evolutivo, avalia seu desempenho usando dados de alta dimensão e compara seu desempenho com classificadores evolutivos e clássicos representativos do estado da arte na área. O neuro classificador processa fluxos de dados continuamente e determina a estrutura de uma rede neural artificial e seus respectivos pesos sinápticos. Os resultados de simulação sugerem que o algoritmo proposto é competitivo quando comparado com os modelos evolutivos analisados nesta dissertação. Ele supera, em termos de taxa de classificação, todos os modelos na maioria dos conjuntos de dados considerados. Ainda, o neuro classificador requer um menor tempo de processamento por amostra entre os classificadores evolutivos e os clássicos não evolutivosAbstract: Evolving systems and high dimensional stream data processing algorithms are of enormous practical importance, and currently are under intensive investigation. This dissertation in- troduces an evolving neural classifying approach, evaluates its performance using high dimensional data, and compare its performance with evolving and classic classifier algorithms representative of the state of the art. The evolving neural classifier works in one-pass mode to find the neural network structure and its weights using high dimensional stream data. The results achieved by the proposed approach suggests that it is competitive with the evolving models addressed in this dissertation. It outperforms in classification rate all of them in most of the datasets considered. Also, the approach requires the lowest per sample processing time amongst the evolving and classic batch classifiersMestradoAutomaçãoMestre em Engenharia Elétrica156374/2014-5CNP

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

Real-time Nonlinear Modeling Of A Twin Rotor Mimo System Using Evolving Neuro-fuzzy Network

This paper presents an evolving neuro-fuzzy network approach (eNFN) to model a twin rotor MIMO system (TRMS) with two degrees of freedom in real-time. The TRMS is a fast, nonlinear, open loop unstable time-varying dynamic system, with cross coupling between the rotors. Modeling and control of TRMS require high sampling rates, typically in the order of milliseconds. Actual laboratory implementation shows that eNFN is fast, effective, and accurately models the TRMS in real-time. The eNFN captures the TRMS system dynamics quickly, and develops precise low cost models from the point of view of time and space complexity. The results suggest eNFN as a potential candidate to model complex, fast time-varying dynamic systems in real-time.Afruz, J., Alam, M., Non-linear modeling of a twin rotor system using particle swarm optimization (2010) Proceedings of the International Computer Symposium, ICS'10, pp. 1026-1032Toha, S., Tokhi, M., ANFIS modelling of a twin rotor system using particle swarm optimization and RLS (2010) Proceedings of the IEEE 9th International Conference on Cybernetic Intelligent Systems, CIS'10, pp. 1-6Angelov, P., Filev, D., Kasabov, N., Guest editorial evolving fuzzy systems: Preface to the special section (2008) IEEE Transactions on Fuzzy Systems, 16 (6), pp. 1390-1392Lemos, A., Caminhas, W., Gomide, F., Adaptive fault detection and diagnosis using an evolving fuzzy classifier (2013) Information Sciences, 220 (0), pp. 64-85Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N., A review of process fault detection and diagnosis: Part I: Quantitative model-based methods (2003) Computers & Chemical Engineering, 27 (3), pp. 293-311Feedback, I., (2006) Twin Rotor MIMO System Control Experiments, pp. 33-942s. , UKToha, S., Tokhi, M., Dynamic nonlinear inverse-model based control of a twin rotor system using adaptive neuro-fuzzy inference system (2009) Proceedings of the Third UKSim European Symposium on Computer Modeling and Simulation, EMS'09, pp. 107-111Nejjari, F., Rotondo, D., Puig, V., Innocenti, M., Quasi-LPV modelling and non-linear identification of a twin rotor system (2012) Proceedings of the 20th Mediterranean Conference on Control Automation, pp. 229-234Subudhi, B., Jena, D., Nonlinear system identification of a twin rotor MIMO system (2009) Proceedings of the IEEE Region 10 Conference, TENCON'09, pp. 1-6Rahideh, A., Shaheed, M., Dynamic modelling of a twin rotor MIMO system using grey box approach (2008) Proceedings of the 5th International Symposium on Mechatronics and Its Applications, ISMA'08, pp. 1-6Aldebrez, F., Darus, I., Tokhi, M., Dynamic modelling of a twin rotor system in hovering position (2004) Proceedings of the First International Symposium on Control, Communications and Signal Processing, pp. 823-826Maciel, L., Lemos, A., Gomide, F., Ballini, R., Evolving fuzzy systems for pricing fixed income options (2012) Evolving Systems, 3 (1), pp. 5-18Lughofer, E., On-line assurance of interpretability criteria in evolving fuzzy systems-achievements, new concepts and open issues (2013) Information Sciences, 251 (0), pp. 22-46Pratama, M., Anavatti, S., Lughofer, E., Evolving fuzzy rule-based classifier based on GENEFIS (2013) Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE'13, pp. 1-8Tung, S., Quek, C., Guan, C., ET2FIS: An evolving type-2 neural fuzzy inference system (2013) Information Sciences, 220 (0), pp. 124-148Cernuda, C., Lughofer, E., Marzinger, W., Kasberger, J., NIRbased quantification of process parameters in polyetheracrylat (PEA) production using flexible non-linear fuzzy systems (2011) Chemometrics and Intelligent Laboratory Systems, 109 (1), pp. 22-33Cernuda, C., Lughofer, E., Suppan, L., Roder, T., Schmuch, R., Hintenaus, P., Marzinger, W., Kasberger, J., Evolving chemometric models for predicting dynamic process parameters in viscose production (2012) Analytica Chimica Acta, 725 (0), pp. 22-38Smith, F., Tighe, A., Adapting in an uncertain world (2005) Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 6, pp. 5958-5963Barros, J., Dexter, A., Evolving fuzzy model-based adaptive control (2007) Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE'07, pp. 1-5Angelov, P., Zhou, X., Filev, D., Lughofer, E., Architectures for evolving fuzzy rule-based classifiers (2007) Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2050-2055Lughofer, E., On-line incremental feature weighting in evolving fuzzy classifiers (2011) Fuzzy Sets Systems, 163 (1), pp. 1-23Iglesias, J., Angelov, P., Ledezma, A., Sanchis, A., Modelling evolving user behaviours (2009) Proceedings of the IEEE Workshop on Evolving and Self-Developing Intelligent Systems, ESDIS'09, pp. 16-23Lemos, A., Caminhas, W., Gomide, F., Fuzzy multivariable gaussian evolving approach for fault detection and diagnosis (2010) Computational Intelligence for Knowledge-Based Systems Design, Ser. Lecture Notes in Computer Science, 6178, pp. 360-369Lughofer, E., Macian, V., Guardiola, C., Klement, E., Identifying static and dynamic prediction models for NOx emissions with evolving fuzzy systems (2011) Applied Soft Computing, 11 (2), pp. 2487-2500Leite, D., Ballini, R., Costa, P., Gomide, F., Evolving fuzzy granular modeling from nonstationary fuzzy data streams (2012) Evolving Systems, 3, pp. 65-79Rahideh, A., Shaheed, M., Robust model predictive control of a twin rotor MIMO system (2009) Proceedings of IEEE International Conference on the Mechatronics, ICM'09, pp. 1-6Rahideh, A., Shaheed, M., Huijberts, H., Stable adaptive model predictive control for nonlinear systems (2008) Proceedings of the American Control Conference, pp. 1673-1678Rahideh, A., Bajodah, A., Shaheed, M., Real time adaptive nonlinear model inversion control of a twin rotor MIMO system using neural networks (2012) Engineering Applications of Artificial Intelligence, 25 (6), pp. 1289-1297Toha, S., Tokhi, M., Inverse model based control for a twin rotor system (2010) Proceedings of the IEEE 9th International Conference on Cybernetic Intelligent Systems, CIS'10, pp. 1-5Silva, A., Caminhas, W., Lemos, A., Gomide, F., A fast learning algorithm for evolving neo-fuzzy neuron (2014) Applied Soft Computing, Part B, 14 (0), pp. 194-209Yamakawa, T., Uchino, E., Miki, T., Kusabagi, H., A neo fuzzy neuron and its applications to system identification and predictions to system behavior (1992) Proceedings of the International Conference on Fuzzy Logic and Neural Networks, 1, pp. 477-484Caminhas, W., Gomide, F., A fast learning algorithm for neofuzzy networks (2000) Proceedings of the Information Processing and Management of Uncertainty in Knowledge Based Systems, IPMU'00, 1 (1), pp. 1784-1790Lemos, A., Caminhas, W., Gomide, F., Fuzzy evolving linear regression trees (2011) Evolving Systems, 2, pp. 1-14Mathworks, I., (2009) Real-time Workshop 7 Users Guide, , Natick, MA, USAKasabov, N., Song, Q., Denfis: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction (2002) IEEE Transactions on Fuzzy Systems, 10 (2), pp. 144-154Angelov, P., Filev, D., An approach to online identification of takagi-sugeno fuzzy models (2004) IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 34 (1), pp. 484-498Angelov, P., Zhou, X., Evolving fuzzy systems from data streams in real-time (2006) Proceedings of the International Symposium on Evolving Fuzzy Systems, pp. 29-3