Title :
Training a network with ternary weights using the CHIR algorithm
Author :
Abramson, S. ; Saad, D. ; Marom, E.
Author_Institution :
Fac. of Eng., Tel Aviv Univ., Israel
fDate :
11/1/1993 12:00:00 AM
Abstract :
A modification of the binary weight CHIR algorithm is presented, whereby a zero state is added to the possible binary weight states. This method allows solutions with reduced connectivity to be obtained, by offering disconnections in addition to the excitatory and inhibitory connections. The algorithm has been examined via extensive computer simulations for the restricted cases of parity, symmetry, and teacher problems, which show convergence rates similar to those presented for the binary CHIR2 algorithm, but with reduced connectivity. Moreover, this method expands the set of problems solvable via the binary weight network configuration with no additional parameter requirements
Keywords :
convergence; digital simulation; feedforward neural nets; learning (artificial intelligence); CHIR algorithm; binary CHIR2 algorithm; binary weight network configuration; convergence rates; disconnections; excitatory connections; inhibitory connections; parity; reduced connectivity; symmetry; teacher problems; ternary weights; zero state; Animation; Biology computing; Circuits; Computer networks; Computer vision; Hopfield neural networks; Optimized production technology; Pattern recognition; Physics computing; Visual system;
Journal_Title :
Neural Networks, IEEE Transactions on
