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Common nature of learning between BP-type and Hopfield-type neural networks
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Outgoing claims: the papers this work cites, builds on, or argues with.
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A Matrix Handbook for Statisticians
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Complex-Valued Neural Networks
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Weights and structure determination of multiple-input feed-forward neural network activated by Chebyshev polynomials of Class 2 via cross-validation
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Backpropagation to train an evolving radial basis function neural network
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A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network
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An integrated micromechanical model and BP neural network for predicting elastic modulus of 3-D multi-phase and multi-layer braided composite
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A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm
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Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding
inferredcites →https://doi.org/10.1016/j.nahs.2014.10.001openalex-snapshot
Mittag-Leffler stability of fractional-order Hopfield neural networks
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Zhang neural network, Getz–Marsden dynamic system, and discrete-time algorithms for time-varying matrix inversion with application to robots' kinematic control
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ANNIE—Artificial Neural Network-based Image Encoder
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Fast learning neural networks using Cartesian genetic programming
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Numerical treatment for nonlinear MHD Jeffery–Hamel problem using neural networks optimized with interior point algorithm
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An artificial neural network approach to automatic speech processing
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Dynamics in fractional-order neural networks
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Image receptive fields for artificial neural networks
inferredcites →https://doi.org/10.1016/j.neucom.2014.05.057openalex-snapshot
A unified evolutionary training scheme for single and ensemble of feedforward neural network
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Sufficient conditions for global attractivity of a class of neutral Hopfield-type neural networks
inferredcites →https://doi.org/10.1016/j.neucom.2014.11.068openalex-snapshot
Asymptotic stability of delayed fractional-order neural networks with impulsive effects
inferredcites →https://doi.org/10.1016/j.neucom.2014.12.031openalex-snapshot
Global stability analysis of fractional-order Hopfield neural networks with time delay
inferredcites →https://doi.org/10.1016/j.neucom.2014.12.062openalex-snapshot
Finite-time state estimation for delayed Hopfield neural networks with Markovian jump
inferredcites →https://doi.org/10.1016/j.neunet.2013.10.002openalex-snapshot
Projective synchronization for fractional neural networks
inferredcites →https://doi.org/10.1016/j.patcog.2014.04.026openalex-snapshot
Cross-validation based weights and structure determination of Chebyshev-polynomial neural networks for pattern classification
inferredcites →https://doi.org/10.1016/s0898-1221(96)90473-3openalex-snapshot
Numerical methods using Matlab
inferredcites →https://doi.org/10.1049/iet-ifs.2013.0145openalex-snapshot
Ensemble detection model for profile injection attacks in collaborative recommender systems based on BP neural network
inferredcites →https://doi.org/10.1073/pnas.79.8.2554openalex-snapshot
Neural networks and physical systems with emergent collective computational abilities.
inferredcites →https://doi.org/10.1098/rspa.2011.0668openalex-snapshot
A new variant of the Zhang neural network for solving online time-varying linear inequalities
inferredcites →https://doi.org/10.1109/his.2009.278openalex-snapshot
Euler Neural Network with Its Weight-Direct-Determination and Structure-Automatic-Determination Algorithms
inferredcites →https://doi.org/10.1109/mci.2008.919075openalex-snapshot
On the use of recurrent neural networks to design symmetric ciphers
inferredcites →https://doi.org/10.1109/mci.2012.2215139openalex-snapshot
Novel Recurrent Neural Network for Time-Varying Problems Solving [Research Frontier]
inferredcites →https://doi.org/10.1109/mci.2013.2247895openalex-snapshot
Complex-Valued Neural Networks: Advances and Applications [Book Review]
inferredcites →https://doi.org/10.1109/tac.2009.2023779openalex-snapshot
Performance Analysis of Gradient Neural Network Exploited for Online Time-Varying Matrix Inversion
inferredcites →https://doi.org/10.1109/tcsi.2008.2007065openalex-snapshot
From Zhang Neural Network to Newton Iteration for Matrix Inversion
inferredcites →https://doi.org/10.1109/tnn.2006.880360openalex-snapshot
A New Adaptive Backpropagation Algorithm Based on Lyapunov Stability Theory for Neural Networks
inferredcites →https://doi.org/10.1109/tnn.2008.2002913openalex-snapshot
A Modified Backpropagation Learning Algorithm With Added Emotional Coefficients
inferredcites →https://doi.org/10.1109/tnn.2010.2050781openalex-snapshot
A Novel Recurrent Neural Network With One Neuron and Finite-Time Convergence for $k$-Winners-Take-All Operation
inferredcites →https://doi.org/10.1109/tnn.2010.2098481openalex-snapshot
Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network
inferredcites →https://doi.org/10.1109/tnnls.2011.2178315openalex-snapshot
Global Convergence of Online BP Training With Dynamic Learning Rate
inferredcites →https://doi.org/10.1109/tnnls.2013.2238555openalex-snapshot
Common Nature of Learning Between Back-Propagation and Hopfield-Type Neural Networks for Generalized Matrix Inversion With Simplified Models
inferredcites →https://doi.org/10.1109/tnnls.2014.2311099openalex-snapshot
Existence and Uniform Stability Analysis of Fractional-Order Complex-Valued Neural Networks With Time Delays
inferredcites →https://doi.org/10.1109/tvt.2014.2317736openalex-snapshot
Automotive Internal-Combustion-Engine Fault Detection and Classification Using Artificial Neural Network Techniques
inferredcites →https://doi.org/10.1126/science.3755256openalex-snapshot
Computing with Neural Circuits: A Model
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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