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Titlebook: New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks; Fernando Gaxiola,Patricia Melin,Fevrier Valdez Book 2016 The

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書目名稱New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks
編輯Fernando Gaxiola,Patricia Melin,Fevrier Valdez
視頻videohttp://file.papertrans.cn/665/664865/664865.mp4
概述Proposes a neural network learning method with type-2 fuzzy weight adjustment.Presents a mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights.P
叢書名稱SpringerBriefs in Applied Sciences and Technology
圖書封面Titlebook: New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks; Fernando Gaxiola,Patricia Melin,Fevrier Valdez Book 2016 The
描述In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights..The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. 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..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 cases of prediction for the Mackey-Glass (for ?=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation ap
出版日期Book 2016
關(guān)鍵詞Computational Intelligence; Neural Networks; Type-2 Fuzzy Weight; Back-propagation Algorithm for Neural
版次1
doihttps://doi.org/10.1007/978-3-319-34087-6
isbn_softcover978-3-319-34086-9
isbn_ebook978-3-319-34087-6Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Author(s) 2016
The information of publication is updating

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Fernando Gaxiola,Patricia Melin,Fevrier Valdezopriately chosen effective diameter. Hard spheres are a starting point in the theory of liquids, as an ideal gas is in the theory of gases and a harmonic solid in solid-state physics. Nowadays a lot of data is available from computer simulations of hard spheres and from integral theories of correlat
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duced in most particle sources. For these situations one can describe the systems with the microcanonical ensemble, apart from the short times during which the particles are exposed to some external manipulation (laser light, collisions), and possibly exchange of thermal radiation with the surroundi
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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks
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Book 2016 to a cases of prediction for the Mackey-Glass (for ?=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation ap
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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks978-3-319-34087-6Series ISSN 2191-530X Series E-ISSN 2191-5318
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