Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Analog VLSI and neural systems
Analog VLSI and neural systems
A general framework for parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Information processing in dynamical systems: foundations of harmony theory
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Recurrent neural networks for LU decomposition and Cholesky factorization
Mathematical and Computer Modelling: An International Journal
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We present a highly parallel model for solving the m x n linear system Ax = b, based on the connectionist paradigm of neural networks. Using m + n linear, perceptron-like neurons, the neutral model converges fast to a problem solution, when one exists, or to a least-squares approximation, when the problem is inconsistent. The velocity of convergence of an analog realization of the network does not depend on m or n, but rather on the eigenvalues of A^TA. Simulation results in linear system solution and suggections for applications are given.