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在“Elsevier电子期刊”中，命中：1,933条，耗时：0.0339782 秒

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1.Adaptive projective synchronization for fractional-order T-S fuzzy neural networks with time-delay and uncertain parameters

作者：Shuai Song^a ; Xiaona Song^a ; ^{xiaona_97@163.com" class="auth_mail" title="E-mail the corresponding author} ; Ines Tejado Balsera^b

关键词：Fractional-order systems ; T-S fuzzy neural networks ; Time delay ; Uncertain parameters ; Adaptive projective synchronization

刊名：Optik - International Journal for Light and Electron Optics

出版年：2017

2.An investigation of the suitability of Artificial Neural Networks for the prediction of core and local skin temperatures when trained with a large and gender-balanced database

作者：K. Michael^a ; M.D.P. Garcia-Souto^b ; ^{p.garciasouto@ucl.ac.uk} ; P. Dabnichki^c ; ^{peter.dabnichki@rmit.edu.au}

关键词：ADALINE ; adaptive linear element ; AI ; artificial intelligence ; ANN/NN ; Artificial neural network(s) ; BMI ; body mass index (body mass of the subject divided by the square of the height in m ; i.e. kg/m2) ; Epoch ; a full data cycle ; i.e. each time the network is presented with a new training pattern ; FNN ; fuzzy neural network(s) ; Logsig ; the logistic sigmoid function ; MSE ; mean squared error ; Nodes ; the interconnections between hidden layers in the neural network (equivalent to neuron connections)

刊名：Applied Soft Computing

出版年：2017

3.Novel stability conditions of fuzzy neural networks with mixed delays under impulsive perturbations

作者：Mei-Yan Lin ^{2063933223@qq.com} ; Cheng-De Zheng ; ^{chd4211853@163.com}

关键词：Impulse ; Fuzzy neural networks ; Linear convex combination ; Reciprocal convex combination ; Quadratic convex combination

刊名：Optik - International Journal for Light and Electron Optics

出版年：2017

4.State estimation of T-S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control

作者：M. Syed Ali^a ; ^{syedgru@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; N. Gunasekaran^a ; ^{gunasmaths@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Quanxin Zhu^b ; ^c ; ^{zqx22@126.com" class="auth_mail" title="E-mail the corresponding author}

关键词：Lyapunov method ; Linear matrix inequality ; Sampled-data control ; T&ndash ; S fuzzy neural network ; Time-varying delay

刊名：Fuzzy Sets and Systems

出版年：2017

5.A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks

作者：Elpiniki I. Papageorgiou^a ; ^b ; ^{epapageorgiou@teiste.gr}Author Vitae ; Katarzyna Poczęta^cAuthor Vitae

关键词：Fuzzy cognitive map ; Artificial neural network ; Forecasting ; Time series prediction ; Real coded genetic algorithm

刊名：Neurocomputing

出版年：2017

6.A fuzzy weighted average approach for selecting portfolio of new product development projects

作者：Marcin Relich^a ; ^{m.relich@wez.uz.zgora.pl}Author Vitae ; Pawel Pawlewski^b ; ^{pawel.pawlewski@put.poznan.pl}Author Vitae

关键词：Fuzzy logic ; Neural networks ; Fuzzy neural system ; Multi-criteria decision making ; New product screening

刊名：Neurocomputing

出版年：2017

7.Improving scalability of ART neural networks

作者：Fernando Benites ; ^{Fernando.Benites@uni-konstanz.de}Author Vitae ; Elena Sapozhnikova ^{Elena.Sapozhnikova@uni-konstanz.de}Author Vitae

关键词：Neural networks ; Adaptive resonance theory ; Clustering ; Classification ; Multi-label classification

刊名：Neurocomputing

出版年：2017

8.A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network

作者：Mohammed Falah Mohammed^a ; ^{falah@ump.edu.my} ; Chee Peng Lim^b

关键词：Fuzzy min&ndash ; max model ; Pattern classification ; Hyperbox structure ; Neural network learning

刊名：Neural Networks

出版年：2017

9.Performance analysis of simplified Fuzzy ARTMAP and Probabilistic Neural Networks for identifying structural damage growth

作者：Mario A. de Oliveira^a ; ^{mario.oliveira@cba.ifmt.edu.br} ; ^{mandersoneee@gmail.com} ; Daniel J. Inman^b ; ^{daninman@umich.edu}

关键词：SHM ; Electromechanical impedance ; PZT ; Damage assessment ; Composite materials

刊名：Applied Soft Computing

出版年：2017

10.Fuzzy stochastic neural network model for structural system identification

作者：Xiaomo Jiang^a ; ^{jiang.xiaomo@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Sankaran Mahadevan^b ; ^{sankaran.mahadevan@vanderbilt.edu" class="auth_mail" title="E-mail the corresponding author} ; Yong Yuan^a ; ^{yuany@tongji.edu.cn" class="auth_mail" title="E-mail the corresponding author}

关键词：Bayesian analysis ; System identification ; Stochastic models ; Neural networks ; Damage assessment

刊名：Mechanical Systems and Signal Processing

出版年：2017

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