A Weight-Based Clustering Method

This paper proposes a weight-based self-constructing clustering method for time series data. Self-constructing clustering processes all the data points incrementally. If a data point is not similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it forms a new cluster of its own; (2) otherwise, the point is removed from the cluster it currently belongs to before a new cluster is formed. However, if a data point is similar enough to an existing cluster, then (1) if the point currently does not belong to any cluster, it is added to the most similar cluster; (2) otherwise, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned and considered in the calculations of similarity between data points and clusters. Experimental results show that our proposed approach performs more effectively than other methods for real world time series datasets

Multi-Label Takagi-Sugeno-Kang Fuzzy System

Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels. However,the modeling of the relationship between the features and the labels is critical to the classification performance. To this end, we propose a new multi-label classification method, called Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS), to improve the classification performance. The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The fuzzy system is trained by integrating fuzzy inference based multi-label correlation learning with multi-label regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark multi-label datasets. 1 The results show that the performance of ML-TSK FS is competitive with existing methods in terms of various evaluation metrics, indicating that it is able to model the feature-label relationship effectively using fuzzy inference rules and enhances the classification performance.Comment: This work has been accepted by IEEE Transactions on Fuzzy System

Learning and identification of fuzzy systems

This thesis concentrates on learning and identification of fuzzy systems, and this thesis is composed about learning fuzzy systems from data for regression and function approximation by constructing complete, compact, and consistent fuzzy systems. Fuzzy systems are prevalent to solve pattern recognition problems and function approximation problems as a result of the good knowledge representation. With the development of fuzzy systems, a lot of sophisticated methods based on them try to completely solve pattern recognition problems and function approximation problems by constructing a great diversity of mathematical models. However, there exists a conflict between the degree of the interpretability and the accuracy of the approximation in general fuzzy systems. Thus, how to properly make the best compromise between the accuracy of the approximation and the degree of the interpretability in the entire system is a significant study of the subject.The first work of this research is concerned with the clustering technique on constructing fuzzy models in fuzzy system identification, and this method is a part of clustering based learning of fuzzy systems. As the determination of the proper number of clusters and the appropriate location of clusters is one of primary considerations on constructing an effectively fuzzy model, the task of the clustering technique aims at recognizing the proper number of clusters and the appropriate location as far as possible, which gives a good preparation for the construction of fuzzy models. In order to acquire the mutually exclusive performance by constructing effectively fuzzy models, a modular method to fuzzy system identification based on a hybrid clustering-based technique has been considered. Due to the above reasons, a hybrid clustering algorithm concerning input, output, generalization and specialization has hence been introduced in this work. Thus, the primary advantage of this work is the proposed clustering technique integrates a variety of clustering properties to positively identify the proper number of clusters and the appropriate location of clusters by carrying out a good performance of recognizing the precise position of each dataset, and this advantage brings fuzzy systems more complete.The second work of this research is an extended work of the first work, and two ways to improve the original work have been considered in the extended work, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned, the purpose of which aims at refining rule base by the similarity analysis of fuzzy sets, fuzzy numbers, fuzzy membership functions or fuzzy rules. By other means, through the similarity analysis of which, the complete rules can be kept and the redundant rules can be reduced probably in the rule base of fuzzy systems. Also, the optimization scheme can be regarded as a two-layer parameters optimization in the extended work, because the parameters of the initial fuzzy model have been fine tuning by two phases gradation on layer. Hence, the extended work primarily puts focus on enhancing the performance of the initial fuzzy models toward the positive reliability of the final fuzzy models. Thus, the primary advantage of this work consists of the simplification of fuzzy rule base by the similarity-based pruning strategy, as well as more accuracy of the optimization by the two-layer optimization scheme, and these advantages bring fuzzy systems more compact and precise.So far as a perfect modular method for fuzzy system identification is concerned, in addition to positively solve pattern recognition problems and function approximation problems, it should primarily comprise the following features, including the well-understanding interpretability, low-degree dimensionality, highly reliability, stable robustness, highly accuracy of the approximation, less computational cost, and maximum performance. However, it is extremely difficult to meet all of these conditions above. Inasmuch as attaining the highly achievement from the features above as far as possible, the research works of this thesis try to present a modular method concerning a variety of requirements to fuzzy systems identification.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Neurofuzzy approach for nonlinear dynamical systems modeling

Orientadores: Fernando Antonio Campos Gomide, Pyramo Pires Costa JuniorTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Este trabalho propõe um procedimento sistemático para obtenção de modelos de sistemas dinâmicos não-lineares complexos utilizando redes neurais nebulosas. As redes neurais nebulosas aplicadas em modelagem são capazes de extrair conhecimento de dados entrada/saída e representar este conhecimento na forma de regras nebulosas do tipo se-então, gerando modelos lingüísticos convenientes para compreensão humana. Duas novas classes de redes neurais nebulosas são propostas a partir de generalizações dos neurônios lógicos AND e OR. Estas generalizações, denominadas unineurons e nullneurons, implementam, além da plasticidade sináptica, outra importante característica dos neurônios biológicos, a plasticidade neuronal. Desta forma, os neurônios propostos neste trabalho são capazes de modificar parâmetros internos em resposta à alterações, permitindo que unineurons e nullneurons variem individualmente de um neurônio AND para um neurônio OR (e vice-e-versa), dependendo da necessidade do problema. Conseqüentemente, uma rede neural nebulosa composta por unineurons e nullneurons é mais geral do que as redes neurais nebulosas similares sugeridas na literatura. Além da introdução de redes neurais com unineurons e nullneurons, um novo algoritmo de treinamento para obtenção de modelos nebulosos de sistemas dinâmicos é proposto utilizando aprendizado participativo. Neste algoritmo, uma nova informação fornecida à rede por meio de um dado entrada/saída é comparada com o conhecimento que já se tem a respeito do sistema. A nova informação só tem influência na atualização do conhecimento se não entrar em conflito com o conhecimento adquirido anteriormente. Como conseqüência, redes neurais nebulosas que utilizam este novo algoritmo de treinamento são mais robustas a dados de treinamento com valores que correspondem a comportamentos anômalos ou mesmo a erros durante a obtenção destes dados. As abordagens propostas foram utilizadas para desenvolver modelos para previsão de séries temporais e modelagem térmica de transformadores de potência. Os resultados experimentais mostram que os modelos aqui propostos são mais robustos e apresentam os melhores desempenhos, tanto em termos de precisão quanto em termos de custos computacionais, quando comparados com abordagens alternativas sugeridas na literaturaAbstract: This work suggests a systematic procedure to develop models of complex nonlinear dynamical systems using neural fuzzy networks. The neural fuzzy networks are able to extract knowledge from input/output data and to encode it explicitly in the form of if-then rules. Therefore, linguistic models are obtained in a form suitable for human understanding. Two new classes of fuzzy neurons are introduced to generalize AND and OR logic neurons. These generalized login neurons, called unineurons and nullneurons, provide a mechanism to implement synaptic plasticity and an important characteristic of biological neurons, the neuronal plasticity. Unineurons and nullneurons achieve synaptic and neuronal plasticity modifying their internal parameters in response to external changes. Thus, unineurons and nullneurons may individually vary from a AND neuron to a OR neuron (and vice-versa), depending upon the necessity of the modeling task. Neural fuzzy networks constructed with unineurons and nullneurons are more general than similar fuzzy neural approaches suggested in literature. Training algorithms for neural fuzzy networks with unineurons and nullneurons are also studied. In particular, a new training algorithm based on the participatory learning is introduced to develop fuzzy models of dynamical systems. In the participatory learning algorithm, a new information brought to the network through an input/output data is first compared with the knowledge that the network already has about the model. The new information influences the update of the knowledge only if it does not conflict with the current knowledge. As a result, neural fuzzy networks trained with participatory learning show greater robustness to training data with anomalous values than their counterparts. The neural fuzzy network and training algorithms suggested herein are used to develop time series forecast models and thermal models of power transformers. Experimental results show that the models proposed here are more robust and perform best in terms of accuracy and computational costs when compared against alternative approaches suggested in the literatureDoutoradoAutomaçãoDoutor em Engenharia Elétric