Evaluation on Transfer Efficiency at Integrated Transport Terminals through Multilevel Grey Evaluation

AbstractTransfer efficiency in integrated transportation terminal is greatly important for both passengers and operational companies. In this paper, we proposed various criteria and a hierarchy index system to evaluate the performance of the transfer condition inside Beijing South Railway Station. To make the assessment more scientific, we assign weightings to each of them by integrated weighting method. Then we use an evaluation method, Multi-level Grey Evaluation, to calculate the performance indexes of different transfer modes in the station and further we compare the ranking results of transfer efficiency of different transfer modes

Adaptive input selection and evolving neural fuzzy networks modeling

This paper suggests an evolving approach to develop neural fuzzy networks for system modeling. The approach uses an incremental learning procedure to simultaneously select the model inputs, to choose the neural network structure, and to update the network weights. Candidate models with larger and smaller number of input variables than the current model are constructed and tested concurrently. The procedure employs a statistical test in each learning step to choose the best model amongst the current and candidate models. Membership functions can be added or deleted to adjust input space granulation and the neural network structure. Granulation and structure adaptation depend of the modeling error. The weights of the neural networks are updated using a gradient-descent algorithm with optimal learning rate. Prediction and nonlinear system identification examples illustrate the usefulness of the approach. Comparisons with state of the art evolving fuzzy modeling alternatives are performed to evaluate performance from the point of view of modeling error. Simulation results show that the evolving adaptive input selection modeling neural network approach achieves as high as, or higher performance than the remaining evolving modeling methods81314CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE MINAS GERAIS - FAPEMIG305906/2014-3não temnão te

An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams

Existing FNNs are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep FNN, namely DEVFNN. Fuzzy rules can be automatically extracted from data streams or removed if they play limited role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely gClass, drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of dimensionality of input space due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using seven datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four popular continual learning algorithms and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy. Moreover, it is also shown that the concept drift detection method is an effective tool to control the depth of network structure while the hidden layer merging scenario is capable of simplifying the network complexity of a deep network with negligible compromise of generalization performance.Comment: This paper has been published in IEEE Transactions on Fuzzy System

Input significance analysis: feature selection through synaptic weights manipulation for EFuNNs classifier

This work is interested in ISA methods that can manipulate synaptic weights namelyConnection Weights (CW) and Garson’s Algorithm (GA) and the classifier selected isEvolving Fuzzy Neural Networks (EFuNNs). Firstly, it test FS method on a dataset selectedfrom the UCI Machine Learning Repository and executed in an online environment, recordthe results and compared with the results that used original and ranked data from the previouswork. This is to identify whether FS can contribute to improved results and which of the ISAmethods mentioned above that work well with FS, i.e. give the best results. Secondly, to attestthe FS results by using a differently selected dataset taken from the same source and in thesame environment. The results are promising when FS is applied, some efficiency andaccuracy are noticeable compared to the original and ranked data.Keywords: feature selection; feature ranking; input significance analysis; evolvingconnectionist systems; evolving fuzzy neural network; connection weights; Garson’salgorithm

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

MODELO FUZZY EVOLUTIVO PARA DETECÇÃO E CLASSIFICAÇÃO EM TEMPO REAL DE DISTÚRBIOS NA QUALIDADE DE ENERGIA ELÉTRICA

Os distúrbios de qualidade de energia elétrica levam a vários inconvenientes, como um aumento da tensão no sistema e nos equipamentos e consequentes perdas; limitação da capacidade de produção; temperaturas operacionais mais altas, falhas prematuras e redução da expectativa de vida das máquinas; mau funcionamento do equipamento e interrupções não planejadas. A detecção e classificação em tempo real de distúrbios são de grande importância para os sistemas de energia. Este artigo propõe o modelo fuzzy evolutivo Takagi-Sugeno (eTS) para a detecção de distúrbios combinado com um método híbrido de seleção de características utilizando o filtro Hodrick-Prescott e a Transformada Rápida de Fourier aplicados sobre uma janela deslizante de sinais de tensão. Os distúrbios spike, notch, inter-harmônico, interrupção curta e harmônico foram considerados. O desempenho de classificação em termos da raiz quadrada do erro quadrático médio (RMSE) e do índice de erro não dimensional (NDEI) mostrou resultados encorajadores. Além disso, o sistema de monitoramento de distúrbios eTS proposto, baseado em fluxo de dados, mostrou ser capaz de aprender novos padrões de distúrbios automaticamente pela adaptação on-line dos parâmetros e estrutura das regras fuzzy. AbstractPower quality disturbances lead to several drawbacks such as an increase in line and equipment voltage and consequent ohmic losses; limitation of the production capacity; higher operating temperatures, premature fails, and reduction of life expectancy of machines; malfunction of equipment; and unplanned outages. Real-time detection and classification of disturbances are of great importance for power systems. This paper proposes an evolving Takagi-Sugeno fuzzy model (eTS) framework for disturbance detection combined with a hybrid Hodrick-Prescott and Fast Fourier Transform feature selection method applied over a sliding window of voltage signals. The spike, notch, inter-harmonic, short interruption and harmonic disturbances were considered. Classification performance in terms of the root mean squared error (RMSE) and non-dimensional error index (NDEI) have shown encouraging results. Moreover, the proposed data stream-based eTS disturbance monitoring system has shown to be able to learn new disturbance patterns automatically by online adapting the parameters and structure of fuzzy rules

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

Scaffolding type-2 classifier for incremental learning under concept drifts

© 2016 Elsevier B.V. The proposal of a meta-cognitive learning machine that embodies the three pillars of human learning: what-to-learn, how-to-learn, and when-to-learn, has enriched the landscape of evolving systems. The majority of meta-cognitive learning machines in the literature have not, however, characterized a plug-and-play working principle, and thus require supplementary learning modules to be pre-or post-processed. In addition, they still rely on the type-1 neuron, which has problems of uncertainty. This paper proposes the Scaffolding Type-2 Classifier (ST2Class). ST2Class is a novel meta-cognitive scaffolding classifier that operates completely in local and incremental learning modes. It is built upon a multivariable interval type-2 Fuzzy Neural Network (FNN) which is driven by multivariate Gaussian function in the hidden layer and the non-linear wavelet polynomial in the output layer. The what-to-learn module is created by virtue of a novel active learning scenario termed the uncertainty measure; the how-to-learn module is based on the renowned Schema and Scaffolding theories; and the when-to-learn module uses a standard sample reserved strategy. The viability of ST2Class is numerically benchmarked against state-of-the-art classifiers in 12 data streams, and is statistically validated by thorough statistical tests, in which it achieves high accuracy while retaining low complexity

Fuzzy Natural Logic in IFSA-EUSFLAT 2021

The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications