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Simulations of Artificial Neural Network With Memristive Devices

Masters Thesis, Electrical Engineering, Boise State University, August 2012

Thahn Tran


The memristor has been hypothesized to exist as the missing fourth basic circuit element
since 1971 [1]. A memristive device is a new type of electrical device that behaves
like a resistor, but can change and remember its internal resistance. This behavior makes
memristive devices ideal for use as network weights, which will need to be adjusted as the
network tries to acquire correct outputs through a learning process. Recent development
of physical memristive-like devices has led to an interest in developing artificial neural
networks with memristors.
In this thesis, a circuit for a single node network is designed to be re-configured into
linearly separable problems: AND, NAND, OR, and NOR. This was done with fixed weight
resistors, programming the memristive devices to pre-specified values, and finally learning
of the resistances through the Madaline Rule II procedure. A network with multiple layers
is able to solve difficult problems or recognize more complex patterns. To illustrate this,
the XOR problem has been used as a benchmark for the multilayer neural network circuit.
The circuit was designed and learning of the weight values was successfully shown.

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