LEADER 03950nam 22005775 450 001 9910254243603321 005 20200702051105.0 010 $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 $aSpringerBriefs in Computational Intelligence,$x2625-3704 300 $aDescription based upon print version of record. 311 $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,$x2625-3704 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$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 LEADER 02098nam 22004453 450 001 9910733724803321 005 20231110215226.0 010 $a3-031-11112-5 035 $a(MiAaPQ)EBC7042224 035 $a(Au-PeEL)EBL7042224 035 $a(CKB)24242656700041 035 $a(BIP)084584520 035 $a(PPN)263899217 035 $a(EXLCZ)9924242656700041 100 $a20220716d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRoad Vehicle Automation 9 210 1$aCham :$cSpringer International Publishing AG,$d2022. 210 4$d©2023. 215 $a1 online resource (187 pages) 225 1 $aLecture Notes in Mobility 311 08$aPrint version: Meyer, Gereon Road Vehicle Automation 9 Cham : Springer International Publishing AG,c2022 9783031111112 330 8 $aThis book is the ninth volume of a sub-series on Road Vehicle Automation, published as part of the Lecture Notes in Mobility. It gathers contributions to the Automated Road Transportation Symposium (ARTS), held on July 12-15, 2021, as a fully virtual event, and as a continuation of TRB's annual summer symposia on automated vehicle systems. Written by researchers, engineers and analysts from around the globe, this book offers a multidisciplinary perspectives on the opportunities and challenges associated with automating road transportation. It highlights innovative strategies, including public policies, infrastructure planning and automated technologies, which are expected to foster sustainable and automated mobility in the near future, thus addressing industry, government and research communities alike. 410 0$aLecture Notes in Mobility 610 $aTransportation 676 $a629.22 676 $a629.22 700 $aMeyer$b Gereon$01370299 701 $aBeiker$b Sven$01370300 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910733724803321 996 $aRoad Vehicle Automation 9$93398480 997 $aUNINA