國立虎尾科技大學 |

New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks/ by Fernando Gaxiola, Patricia Melin, Fevrier Valdez.
作者:	Gaxiola, Fernando.
其他作者:	Melin, Patricia.
面頁冊數:	IX, 102 p. 94 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-34087-6
ISBN:	9783319340876

New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks
Gaxiola, Fernando.

New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks[electronic resource] /by Fernando Gaxiola, Patricia Melin, Fevrier Valdez. - 1st ed. 2016. - IX, 102 p. 94 illus.online resource. - SpringerBriefs in Computational Intelligence,2625-3704. - SpringerBriefs in Computational Intelligence,.

Introduction.-Theory and Background -- Problem Statement an Development -- Simulations and Results -- Conclusions.

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 ô=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 approach obtains better behavior and tolerance to noise than the other methods. The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error.

ISBN: 9783319340876

Standard No.: 10.1007/978-3-319-34087-6doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks
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