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How to train a neural network:An introduction to the new computational paradigm
Complexity 1 (6):13-28 (1996)
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Citations of this work
State estimation of memristor‐based recurrent neural networks with time‐varying delays based on passivity theory.R. Rakkiyappan, A. Chandrasekar, S. Laksmanan & Ju H. Park - 2014 - Complexity 19 (4):32-43.
Exponential state estimator design for discrete-time neural networks with discrete and distributed time-varying delays.Qihui Duan, Ju H. Park & Zheng-Guang Wu - 2014 - Complexity 20 (1):38-48.
Mean square exponential synchronization for impulsive coupled neural networks with time-varying delays and stochastic disturbances.Ze Tang, Ju H. Park, Tae H. Lee & Jianwen Feng - 2016 - Complexity 21 (5):190-202.
Exponential stability for markovian jumping stochastic BAM neural networks with mode-dependent probabilistic time-varying delays and impulse control.R. Rakkiyappan, A. Chandrasekar, S. Lakshmanan & Ju H. Park - 2015 - Complexity 20 (3):39-65.
A look inside the learning process of neural networks.K. Bertels, L. Neuberg, S. Vassiliadis & G. Pechanek - 2000 - Complexity 5 (6):34-38.
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