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33
FRIwE: Fuzzy Rule Identification With Exceptions
"... Abstract—In this paper, the FRIwE method is proposed to identify fuzzy models from examples. Such a method has been developed trying to achieve a double goal:accuracy and interpretability. In order to do that, maximal structure fuzzy rules are firstly obtained based on a method proposed by Castro et ..."
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Abstract—In this paper, the FRIwE method is proposed to identify fuzzy models from examples. Such a method has been developed trying to achieve a double goal:accuracy and interpretability. In order to do that, maximal structure fuzzy rules are firstly obtained based on a method proposed by Castro et al. In a second stage, the conflicts generated by the maximal rules are solved, thus increasing the model accuracy. The resolution of conflicts are carried out by including exceptions in the rules. This strategy has been identified by psychologists with the learning mechanism employed by the human being, thus improving the model interpretability. Besides, in order to improve the interpretability even more, several methods are presented based on reducing and merging rules and exceptions in the model. The exhaustive use of the training examples gives the method a special suitability for problems with small training sets or high dimensionality. Finally, the method is applied to an example in order to analyze the achievement of the goals. Index Terms—Conflicting rules, fuzzy model identification, interpretability, maximal rules, rule simplification. I.
Online global learning in direct fuzzy controllers
 IEEE Transactions on Fuzzy Systems
, 2004
"... Abstract—A novel approach to achieve realtime global learning in fuzzy controllers is proposed. Both the rule consequents and the membership functions defined in the premises of the fuzzy rules are tuned using a onestep algorithm, which is capable of controlling nonlinear plants with no prior offl ..."
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Abstract—A novel approach to achieve realtime global learning in fuzzy controllers is proposed. Both the rule consequents and the membership functions defined in the premises of the fuzzy rules are tuned using a onestep algorithm, which is capable of controlling nonlinear plants with no prior offline training. Direct control is achieved by means of two auxiliary systems: The first one is responsible for adapting the consequents of the main controller’s rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input–output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed, avoiding overfitting. Index Terms—Complete rulebased fuzzy systems, fuzzy control, global learning, realtime direct control. I.
Automatic ClusteringBased Identification of Autoregressive Fuzzy Inference Models for Time Series
"... We analyze the use of clustering methods for the automatic identification of fuzzy inference models for autoregressive prediction of time series. A methodology that combines fuzzy methods and residual variance estimation techniques is followed. A nonparametric residual variance estimator is used for ..."
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We analyze the use of clustering methods for the automatic identification of fuzzy inference models for autoregressive prediction of time series. A methodology that combines fuzzy methods and residual variance estimation techniques is followed. A nonparametric residual variance estimator is used for a priori input and model selection. A simple scheme for initializing the widths of the input membership functions of fuzzy inference systems is proposed for the Improved Clustering for Function Approximation algorithm (ICFA), previously introduced for initializing RBF networks. This extension to the ICFA algorithm is shown to provide the most accurate predictions among a wide set of clustering algorithms. The method is applied to a diverse set of time series benchmarks. Its advantages in terms of accuracy and computational requirements are shown as compared to leastsquares support vector machines (LSSVM), the multilayer perceptron (MLP) and two variants of the extreme learning machine (ELM).
M.: MultiGridBased Fuzzy Systems for Function Approximation. LNCS/LNAI MICAI’2004
"... Abstract. In this paper we make use of a modified Grid Based Fuzzy System architecture, which may provide an exponential reduction in the number of rules needed. We also introduce an algorithm that automatically, from a set of given I/O training points, is able to determine the pseudooptimal archit ..."
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Abstract. In this paper we make use of a modified Grid Based Fuzzy System architecture, which may provide an exponential reduction in the number of rules needed. We also introduce an algorithm that automatically, from a set of given I/O training points, is able to determine the pseudooptimal architecture proposed as well as the optimal parameters needed (number and position of membership functions and fuzzy rule consequents). The suitability of the algorithm and the improvement in both performance and efficiency obtained are shown in an example. 1
Extracting fuzzy rules from polysomnographic recordings for infant sleep classification
 IEEE Transactions on BioMedical Engineering
"... Abstract—A neurofuzzy classifier (NFC) of sleepwake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakef ..."
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Abstract—A neurofuzzy classifier (NFC) of sleepwake states and stages has been developed for healthy infants of ages 6 mo and onward. The NFC takes five input patterns previously identified on 20s epochs from polysomnographic recordings and assigns them to one out of five possible classes: Wakefulness, REMSleep, NonREM Sleep Stage 1, Stage 2, and Stage 34. The definite criterion for a sleep state or stage to be established is duration of at least 1 min. The data set consisted of a total of 14 continuous recordings of naturally occurring naps (average duration: 143 39 min), corresponding to a total of 6021 epochs. They were divided in a training, a validation and a test set with 7, 2, and 5 recordings, respectively. During supervised training, the system determined the fuzzy concepts associated to the inputs and the rules required for performing the classification, extracting knowledge from the training set, and pruning nonrelevant rules. Results on an independent test set achieved 83 9 0 4 % of expert agreement. The fuzzy rules obtained from the training examples without a priori information showed a high level of coincidence with the crisp rules stated by the experts, which are based on internationally accepted criteria. These results show that the NFC can be a valuable tool for implementing an automated sleepwake classification system. Index Terms—ANFIS, fuzzy rule extraction, knowledge discovery, neural nets and expert systems, rule pruning, sleep classification. I.
MultiGridBased Fuzzy Systems for Time Series Forecasting: Overcoming the curse of dimensionality
"... Abstract. This work introduces a modified Grid Based Fuzzy System architecture, which is especially suited for the problem of time series prediction. This new architecture overcomes the problem inherent to all gridbased fuzzy systems when dealing with high dimensional input data. This new architect ..."
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Abstract. This work introduces a modified Grid Based Fuzzy System architecture, which is especially suited for the problem of time series prediction. This new architecture overcomes the problem inherent to all gridbased fuzzy systems when dealing with high dimensional input data. This new architecture together with the proposed algorithm allows the possibility of incorporating a higher number of input variables, keeping low both the computational complexity of the algorithm and the complexity of the architecture. 1
A Hybrid Learning Algorithm for Fuzzy Neural Networks ∗
"... We propose a novel hybrid learning algorithm for fuzzy neural networks. The algorithm consists of the gradient descent method and a recursive SVDbased least squares estimator, which are used to refine the premise and consequent parameters, respectively. The advantages of our method are that the co ..."
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We propose a novel hybrid learning algorithm for fuzzy neural networks. The algorithm consists of the gradient descent method and a recursive SVDbased least squares estimator, which are used to refine the premise and consequent parameters, respectively. The advantages of our method are that the consequent parameters are updated optimally and that the search space of backpropagation for premise parameters is greatly reduced. As a result, our algorithm converges more quickly and produces smaller errors than the pure gradient descent method. 1
International Journal of Approximate Reasoning
, 2006
"... www.elsevier.com/locate/ijar Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multiobjective evolutionary algorithms q ..."
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www.elsevier.com/locate/ijar Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multiobjective evolutionary algorithms q
unknown title
, 2005
"... www.elsevier.com/locate/fss TaSe, a Taylor seriesbased fuzzy system model that combines interpretability and accuracy ..."
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www.elsevier.com/locate/fss TaSe, a Taylor seriesbased fuzzy system model that combines interpretability and accuracy
unknown title
, 2004
"... www.elsevier.com/locate/fss Multiobjective hierarchical genetic algorithm for interpretable fuzzy rulebased knowledge extraction � ..."
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www.elsevier.com/locate/fss Multiobjective hierarchical genetic algorithm for interpretable fuzzy rulebased knowledge extraction �