Object Recognition Based on Nano Memristive Neural Network with Variable Structure
Many Memristive neural network arrays that have been designed in recent years are simultaneously dealt with all of their synapses in working status. Therefore, when a relatively small-scale neural network is implemented with these memristor arrays, some of these synapses which are not
used may cause errors in the result due to the impact of unexpected interference signals, and it can also cause some unnecessary energy consumption. In this paper, a memristive neural network with variable network structure is investigated. Based on this network, the number of synapses involved
in the work can be flexibly adjusted to improve system performance. Two different scales of neural networks are simulated in Pspice to prove the feasibility and effectiveness of the proposed memristive neural network structure.
Keywords: CIRCUIT; MEMRISTOR; NANO; NEURAL NETWORK; OBJECT RECOGNITION
Document Type: Research Article
Publication date: 01 April 2020
- Journal of Nanoelectronics and Optoelectronics (JNO) is an international and cross-disciplinary peer reviewed journal to consolidate emerging experimental and theoretical research activities in the areas of nanoscale electronic and optoelectronic materials and devices into a single and unique reference source. JNO aims to facilitate the dissemination of interdisciplinary research results in the inter-related and converging fields of nanoelectronics and optoelectronics.
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