Outliers and gross errors in training data sets can seriously deteriorate the performance of traditional supervised feedforward neural networks learningalgorithms. This is why several learning methods, to some extent...
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Outliers and gross errors in training data sets can seriously deteriorate the performance of traditional supervised feedforward neural networks learningalgorithms. This is why several learning methods, to some extent robust to outliers, have been proposed. In this paper we present a new robustlearning algorithm based on the iterative Least Median of Squares, that outperforms some existing solutions in its accuracy or speed. We demonstrate how to minimise new non-differentiable performance function by a deterministic approximate method. Results of simulations and comparison with other learning methods are demonstrated. Improved robustness of our novel algorithm, for data sets with varying degrees of outliers, is shown.
Gross errors and outliers in the feedforward neural networks training sets may often corrupt the performance of traditional learningalgorithms. Such algorithms try to fit networks to the contaminated data, so the res...
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Gross errors and outliers in the feedforward neural networks training sets may often corrupt the performance of traditional learningalgorithms. Such algorithms try to fit networks to the contaminated data, so the resulting model may be far from the desired one. In this paper we propose new, robust to outliers, learning algorithm based on the concept of the least trimmed absolute value (LTA) estimator. The novel LTA algorithm is compared with traditional approach and other robustlearning methods. Experimental results, presented in this article, demonstrate improved performance of the proposed training framework, especially for contaminated training data sets. (c) 2013 Elsevier B.V. All rights reserved.
Artificial neural networks (ANNs) have been frequently used in forecasting problems in recent years. One of the most popular types of ANNs in these days is Pi-Sigma artificial neural networks (PS-ANNs). PS-ANNs have a...
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Artificial neural networks (ANNs) have been frequently used in forecasting problems in recent years. One of the most popular types of ANNs in these days is Pi-Sigma artificial neural networks (PS-ANNs). PS-ANNs have a high order ANN structure and they use both multiplicative and additive neuron models in their architecture. PS-ANNs produce superior forecasting performance because of their high order structure. PS-ANNs are affected negatively by an outlier or outliers in a data set because of having a multiplicative neuron model in their architecture. In this study, a new robustlearning algorithm based on particle swarm optimization and Huber's loss function for PS-ANNs is proposed. To evaluate the performance of the proposed method, Dow Jones stock exchange and Australian beer consumption data sets are analyzed and the obtained results are compared with many ANNs types proposed in the literature. Besides, the performance of the proposed method in outlier cases is also investigated by injecting outliers into these data sets. It is seen that the proposed learning algorithm has a satisfying performance both the data have an outlier or outliers' case and original case.
The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Various approaches for modeling TSK fuzzy rules ...
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The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Various approaches for modeling TSK fuzzy rules have been proposed in the literature. Most of them define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, in real world applications, training data sets often contain outliers. When outliers exist, traditional clustering and learningalgorithms based on the principle of least square error minimization may be seriously affects by outliers. Various robust approaches have been proposed to solve this problem in the neural networks and pattern recognition community. In this paper, a novel robust TSK fuzzy modeling approach is presented. In the approach, a clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers. To obtain a more precision model, a robust fine-tuning algorithm is then employed. Various examples are used to verify the effectiveness of the proposed approach. From the simulation results, the proposed robust TSK fuzzy modeling indeed showed superior performance over other approaches.
As a very important tool for dealing with both crisp data and fuzzy data, fuzzy regression analysis based on interval regression analysis has become an active area of research. Some neural network related methods for ...
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As a very important tool for dealing with both crisp data and fuzzy data, fuzzy regression analysis based on interval regression analysis has become an active area of research. Some neural network related methods for nonlinear interval regression analysis have been proposed on the assumption that given training data are totally "good" data. The performance of these methods will significantly worsen when the training data are spoiled by outliers. In this paper, we introduce the concepts of polarity and quality of the training data, on the basis of which we propose two robust learning algorithms for determining a robust nonlinear interval regression model, which makes a feature of a new cost function for reflecting not only the polarity of the training data but also the estimated knowledge about the quality of the training data. The two robustalgorithms are derived in a manner similar to the back-propagation (BP) algorithm. Simulation results show that our robustalgorithms outperform the existing methods remarkably in two aspects when outliers are present: (1)They are robust against outliers;(2) Their rates of convergence are improved to some extent. (C) 1998 Elsevier Science B.V. All rights reserved.
In this letter, we propose two robust and distributed game based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem wit...
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In this letter, we propose two robust and distributed game based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem with imperfect information in heterogeneous cellular networks (HCNs). In particular, we repeatedly sample the received payoffs in the exploitation stage of each algorithm to guarantee the convergence when the payoffs of some users (UEs) in [I] cannot accurately be acquired for some reasons. Then, we derive the rational sampling number and prove the convergence of the modified algorithms. Finally, simulation results demonstrate that two modified algorithms achieve good convergence performances and robustness in the incomplete information scheme.
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