eTS fuzzy models for on-line learning
On line evolution of Takagi Sugeno fuzzy models
... ON-LINE LEARNING OF eTS MODELS On-line learning of eTS fuzzy models includes recursive clustering and forming of a gradually evolving rule-base and weighted ...
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On-line evolution of Takagi-Sugeno fuzzy models
... ON-LINE LEARNING OF eTS MODELS On-line learning of eTS fuzzy models includes recursive clustering and forming of a gradually evolving rule-base and weighted ...
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On line identification of MIMO evolving Takagi Sugeno fuzzy models
... (eTS) fuzzy models and the method for their on-line identification has been recently introduced as an effective tool for design of flexible system models with minimum a priori ...
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On-line identification of MIMO evolving Takagi-Sugeno fuzzy models
... (eTS) fuzzy models and the method for their on-line identification has been recently introduced as an effective tool for design of flexible system models with minimum a priori ...
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Forecasting Time-Series for NN GC1 using Evolving Takagi-Sugeno (eTS) Fuzzy Systems with On-line Inputs Selection
... (eTS) Fuzzy Systems [2-6] to self-learn from the time series. ETS are characterized by the fact that they self-learn and evolve the fuzzy rule-based system which, in fact, represents their ...
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ETS: Discriminative Edit Models for Paraphrase Scoring
... Abstract Many problems in natural language process- ing can be viewed as variations of the task of measuring the semantic textual similarity be- tween short texts. However, many systems that address these tasks focus on ...
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On-line evolving fuzzy clustering
... statistical learning theory ...1980’s fuzzy C-means algorithm was proposed by Bezdek [2] as an extension of the K-means algorithm to account for the fuzziness present in a data ...and learning ...
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SELF-LEARNING SCORING MODELS – INTRODUCTION OF AN ON-LINE APPROACH TO RISK ASSESMENT
... the models used for risk management and knowledge of information theory, machine learning and artificial neural ...on- line, using their ability for automatic deduction rules based on data, during ...
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Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine?
... general fuzzy min-max neural network with its basic learning procedure is used within six different algorithm independent learning ...the learning scheme and the potential for adaptation to ...
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Boundedly rational learning and heterogeneous trading strategies with hybrid neuro-fuzzy models
... heterogeneous learning rules in speculative markets where heuristic strategies reflect the rules-of-thumb of boundedly rational ...new models that would enhance forecasting ability particularly for time ...
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On-line modeling and control via T-S fuzzy models for nonaffine nonlinear systems using a second type adaptive fuzzy approach
... (T-S) fuzzy models for nonaffine nonlinear systems, with external ...T-S fuzzy model is established to approximate the nonaffine nonlinear dynamic system in a linearized ...of fuzzy ...
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Evaluation of fuzzy regression models by fuzzy neural network
... network Learning algorithm a b s t r a c t In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy linear and nonlinear ...
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Learning Rate Updating Methods Applied to Adaptive Fuzzy Equalizers for Broadband Power Line Communications
... adaptive fuzzy equalizers with variable step size for broadband power line (PL) ...nonsingleton fuzzy equalizers with variable step size to cope with the intersymbol interference (ISI) effects of PL ...
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Emerging models for international higher education and collaborative learning through on-line graduate study.
... Abstract Organizations are operating in diverse, complex, and expedited environments that have recently been plagued by increased external conflict. As such, organizations require human capital that is well educated and ...
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Role of dutasteride in pre‐clinical ETS fusion‐positive prostate cancer models
... Since androgen induces the expression of TMPRSS2–ERG, in the present study we investigated the effect of dutasteride on the expression of ERG as well as cellular phenotypes of the TMPRSS2–ERG rearrangement-positive ...
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Forward and backward fuzzy economic order quantity models considering learning theory / Ehsan Shekarian
... as learning increases and as it becomes ...a fuzzy inventory model with immediate return for defective ...two fuzzy inventory models in which in the first model the maximum backordering ...
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A Survey on Applications of Neuro-Fuzzy Models
... II. Neural networks due to their abilities in learning new relation with new input data may be utilized for refining fuzzy rules to generate the fuzzy adaptive system. Example 2 The photo voltaic ...
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Learning and identification of fuzzy systems
... of fuzzy systems and the optimization scheme for parameters ...of fuzzy sets, fuzzy numbers, fuzzy membership functions or fuzzy ...initial fuzzy model have been fine tuning by ...
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Fuzzy Process Control And Development Of Some Models For Fuzzy Control Charts
... production line to predict significant deviations that may result in reject ...production line is running normally, and these variances can be analyzed statistically to control the ...packaging line ...
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Fuzzy models in regional statistics
... of fuzzy spatial ...the fuzzy set theory is a useful tool for spatial ...apply fuzzy models in the field, for example, for the assessment of urban air quality ...
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