MAPPING THE BCPNN LEARNING RULE TO A MEMRISTOR MODEL

Mapping the BCPNN Learning Rule to a Memristor Model

The Bayesian Confidence Propagation Neural Network (BCPNN) has been implemented in a way that allows mapping to neural and synaptic processes in the human cortexandhas been used extensively in detailed spiking models of cortical associative 2014 dodge ram 1500 fender flares memory function and recently also for machine learning applications.In conv

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Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network

Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners.Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment.This paper presents a convolutional autoencoder (CAE) network-based method for identifying fas

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Influence of Disc Tip Geometry on the Aerodynamic Performance and Flow Characteristics of Multichannel Tesla Turbines

As a competitive small-scale turbomachinery option, Tesla turbines have wide potential in various fields, such as renewable energy generation systems and small power equipment.This paper investigates the influence of disc tip geometry, including its profile and relative height, on the aerodynamic performance and flow characteristics of one-to-one a

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