Abstract
A learning architecture for memristor-based multilayer neural networks is proposed in this paper. A multilayer neural network is implemented based on memristor bridge synapses and its learning is performed with Random Weight Change architecture. The memristor bridge synapses are composed of bridge type architectures of back-to-back connected 4 memristors and the Random Weight Change (RWC) algorithm is based on a simple trial-and-error learning. Though the RWC algorithm requires more iterations than backpropagation, learning time is two orders faster than that of a software counterpart due to the benefit of circuit-based learning.
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