Happy holidays from us to you—get up to $30 off your next print or eBook! Shop now >>

SpringerBriefs in Computational Intelligence

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

Authors: Gaxiola, Fernando, Melin, Patricia, Valdez, Fevrier

  • 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
  • Presents 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 to illustrate the advantages of the proposed method
see more benefits

Buy this book

eBook 44,02 €
price for Spain (gross)
  • ISBN 978-3-319-34087-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 57,19 €
price for Spain (gross)
  • ISBN 978-3-319-34086-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this book

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.

Table of contents (5 chapters)

Buy this book

eBook 44,02 €
price for Spain (gross)
  • ISBN 978-3-319-34087-6
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 57,19 €
price for Spain (gross)
  • ISBN 978-3-319-34086-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks
Authors
Series Title
SpringerBriefs in Computational Intelligence
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
The Author(s)
eBook ISBN
978-3-319-34087-6
DOI
10.1007/978-3-319-34087-6
Softcover ISBN
978-3-319-34086-9
Series ISSN
2625-3704
Edition Number
1
Number of Pages
IX, 102
Number of Illustrations
94 b/w illustrations
Topics