Journal
INFORMATION SCIENCES
Volume 260, Issue -, Pages 1-14Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2013.11.006
Keywords
Type-2 fuzzy logic; Neural network; Backpropagation; Time series prediction
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In this paper a new backpropagation learning method enhanced with type-2 fuzzy logic is presented. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. In this work, type-2 fuzzy inference systems are used to obtain the type-2 fuzzy weights by applying a different size of the footprint of uncertainty (FOU). The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a case of prediction for the Mackey-Glass time series (for iota = 17). Noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods. (C) 2013 Elsevier Inc. All rights reserved.
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