Oscillations in a refractory neural net

Oscillations in a refractory neural net.

A functional differential equation that arises from the classic theory of neural networks is considered. As the length of the absolute refractory period is varied, there is, as shown here, a super-critical Hopf bifurcation. As the ratio of the refractory period to the time constant of the network increases, a novel relaxation oscillation occurs. Some approximations are made and the period of th...

Missing Values in a Backpropogation Neural Net

An empirical study of methods of handling missing values in a backpropagation neural network is presented. Neural networks can be applied to many real world systems to perform classification, pattern recognition or prediction on the basis of input data. However, many such applications cannot guarantee that the data provided to the network will be complete. The backpropagation network does not l...

Eecient Neural Net --evaluators

cient Neural Net

We show how eld-programable gate arrays can be used to eeciently implement neural nets. By implementing the training phase in software and the actual application in hardware, connicting demands can be met: training beneets from a fast edit-debug cycle, and once the design has stabilized, a hardware implementation results in higher performance. While neural nets have been implemented in hardware...

A Time-derivative Neural Net Architecture - an Alternative to the Time-delay Neural Net Architecture

Though the time-delay neural net architecture has been recently used in a number of speech recognition applications, it has the problem that it can not use longer temporal contexts because this increases the number of connection weights in the network. This is a serious bottleneck because the use of larger temporal contexts can improve the recognition performance. In this paper, a time-derivari...