LEADER 03798nam 22005775 450 001 9910254243603321 005 20251113194136.0 010 $a3-319-34087-5 024 7 $a10.1007/978-3-319-34087-6 035 $a(CKB)3710000000718306 035 $a(EBL)4538002 035 $a(DE-He213)978-3-319-34087-6 035 $a(MiAaPQ)EBC4538002 035 $a(PPN)194379841 035 $a(EXLCZ)993710000000718306 100 $a20160602d2016 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks /$fby Fernando Gaxiola, Patricia Melin, Fevrier Valdez 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (111 p.) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 300 $aDescription based upon print version of record. 311 08$a3-319-34086-7 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aIntroduction.-Theory and Background -- Problem Statement an Development -- Simulations and Results -- Conclusions. 330 $aIn this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods. The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence 606 $aArtificial Intelligence 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3 700 $aGaxiola$b Fernando$4aut$4http://id.loc.gov/vocabulary/relators/aut$0763073 702 $aMelin$b Patricia$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aValdez$b Fevrier$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254243603321 996 $aNew Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks$92540611 997 $aUNINA