Stochastic neural computation. II. Soft competitive learning
Bryce Brown, HOWARD C. CARD · IEEE Transactions on Computers · 2001
For pt. I see ibid., p.891-905. An investigation has been made into the use of stochastic arithmetic to implement an artificial neural network solution to a typical pattern recognition application. Optical character recognition is performed on very noisy characters in the E-13B MICR font. The artificial neural network is composed of two layers, the first layer being a set of soft competitive learning subnetworks and the second a set of fully connected linear output neurons. The observed number of clock cycles in the stochastic case represents an order of magnitude improvement over the floating-point implementation assuming clock frequency parity. Network generalization capabilities were also compared based on the network squared error as a function of the amount of noise added to the input patterns. The stochastic network maintains a squared error within 10 percent of that of the floating-point implementation for a wide range of noise levels.