Modified Hebbian learning for curve and surface fitting
Neural Networks
An Extended Projection Neural Network for Constrained Optimization
Neural Computation
Dynamical system for computing largest generalized eigenvalue
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Self-organizing algorithms for generalized eigen-decomposition
IEEE Transactions on Neural Networks
A class of learning algorithms for principal component analysis and minor component analysis
IEEE Transactions on Neural Networks
A recurrent neural network for solving nonlinear convex programs subject to linear constraints
IEEE Transactions on Neural Networks
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We show some mistakes in the paper ''Recurrent neural network model for computing largest and smallest generalized eigenvalue, Neurocomputing 71 (2008) 3589-3594'' using a counterexample. And another recurrent neural network (RNN) with invariant B-norm is proposed for computing the largest or smallest generalized eigenvalue and the corresponding eigenvector of any symmetric positive pair (A,B), which can be simply extended to compute the second largest or smallest generalized eigenvalue and the corresponding eigenvector based on the similar skills established in other literature. In addition, convergence of such RNN is proven rigorously. Simulation results demonstrate the computational capability of such model.