Implementation of the backpropagation algorithm on iPSC/2 hypercube multicomputer system
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Backpropagation is a supervised learning procedure for a class of artificial neural networks. It has recently been widely used in training such neural networks to perform relatively nontrivial tasks like text-to-speech conversion or autonomous land vehicle control. However, the slow rate of convergence of the basic backpropagation algorithm has limited its application to rather small networks since the computational requirements grow significantly as the network size grows. This thesis work presents a parallel implementation of the backpropagation learning algorithm on a hypercube multicomputer system. The main motivation for this implementation is the construction of a parallel training and simulation utility for such networks, so that larger neural network applications can be experimented with.
KeywordsQA76.58 .E73 1990
Parallel processing (Electronic computers).
Neural networks (Computer science).
Back propagation (Artificial intelligence).
Integrated circuits--Very large scale integration--Design and construction.
Hypercube networks (Computer networks).