Digital Neural Network Research Articles

Implementation issues of sigmoid function and its derivative for VLSI digital neural networks

This paper proposes a number of different implementations for the first derivative of the sigmoid function. The implementation of the sigmoid function employs a powers-of-two piecewise linear approximation. The best implementation scheme for the derivative is suggested based on overall speed performance (circuit speed and training time) and hardware requirements.

Implementing nonlinear activation functions in neural network emulators

An alternative approach to implementing nonlinear activation functions for digital neural networks is presented. Unlike other methods, this approach has the advantage that it is processed by a common arithmetic unit already required for the network computations and it does not require the use of a look-up table.

An analog integrated neural network capable of learning the Feigenbaum logistic map

A description is presented of the design and behavior of analog integrated circuits based on a digital neural network developed and used by A. Lapedes and R. Farber (Tech. Rep. LA-UR-87-26662, Los Alamos Nat. Lab., 1987) to learn the Feigenbaum logistic map. The analog circuits can operate in real time, orders of magnitude faster than the digital model, but it is shown that a direct analog implementation may not be as accurate as the original digital network. An analysis is also presented that shows that the inaccuracies arise not only, as expected, directly from the limited numerical precision of the analog circuit but also indirectly from parameter sensitivity, which is shown to be a problem for this type of neural network. To correct for these nonidealities the analog network includes an adaptive learning circuit. It is demonstrated that, in certain circumstances, the simple analog servomechanism suggested can replace the back-propagation learning algorithm used in digital neural networks. >

Digital neural networks — A distributed architecture with self-evolving capabilities

Digital neural networks — A distributed architecture with self-evolving capabilities