Stock photo: cover may vary
New Backpropagation Algorithm With Type-2 Fuzzy Weights for Neural Networks Paperback - 2016
by Gaxiola, Fernando/ Melin, Patricia/ Valdez, Fevrier
- New
- Paperback
A$123.92
A$48.35
Delivery to USA
Standard delivery: 7 to 14 days
More delivery options
Standard delivery: 7 to 14 days
Ships from Revaluation Books (Devon, United Kingdom)
Details
- Title New Backpropagation Algorithm With Type-2 Fuzzy Weights for Neural Networks
- Author Gaxiola, Fernando/ Melin, Patricia/ Valdez, Fevrier
- Binding Paperback
- Condition New
- Pages 102
- Volumes 1
- Language ENG
- Publisher Springer-Verlag New York Inc
- Publication date 2016
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # x-3319340867
- ISBN 9783319340869 / 3319340867
- Weight 0.38 lbs (0.17 kg)
- Dimensions 9.21 x 6.14 x 0.24 in (23.39 x 15.60 x 0.61 cm)
- Category Computers - General Information
- Dewey Decimal Code 006.3
- Quantity available 2
About Revaluation Books Devon, United Kingdom
Biblio member since 2020
General bookseller of both fiction and non-fiction.
Reader reviews for New Backpropagation Algorithm With Type-2 Fuzzy Weights for Neural Networks
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
From the rear cover
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 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.
