Abstract

Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and often suffer from a number of short-comings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing distributed feedforward networks that use dynamic topologies (dynamic ANNs) efficiently in parallel hardware. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows efficient support for adding and deleting nodes dynamically during learning. In particular, this paper presents an LIT for dynamic Backpropagation networks with a single hidden layer. The complexity of both learning and execution algorithms is O(n+p+logm) for a single pattern, where n is the number of inputs, p is the number of outputs, and m is the number of hidden nodes in the original network.

Citations