A generalized convergence theorem for neural networks

DOI10.1109/18.21239zbMath0666.94024OpenAlexW2122401586MaRDI QIDQ3818997

Jehoshua Bruck, Joseph W. Goodman

Publication date: 1989

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://authors.library.caltech.edu/5705/

zbMATH Keywords

threshold logic function

Mathematics Subject Classification ID

Applications of graph theory to circuits and networks (94C15)

Related Items

RBF-based neurodynamic nearest neighbor classification in real pattern space ⋮ General-Purpose Computation with Neural Networks: A Survey of Complexity Theoretic Results ⋮ Are analog neural networks better than binary neural networks? ⋮ A chaotic discrete-time continuous-state Hopfield network with piecewise-affine activation functions ⋮ On the power of neural networks for solving hard problems ⋮ On the Cost of Simulating a Parallel Boolean Automata Network by a Block-Sequential One ⋮ Asymmetric Boltzmann machines ⋮ Neural network methods in combinatorial optimization ⋮ On the convergence of iterative receiver algorithms utilizing hard decisions ⋮ Convergence and Stability of Quantized Hopfield Networks Operating in a Fully Parallel Mode ⋮ Attractors of Hopfield-type lattice models with increasing neuronal input ⋮ Computing with truly asynchronous threshold logic networks