Hopfield model

Hopfield model

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As for troposphere, we use Hopfield model to get the tropospheric refractive index.
In this paper, we use the Hopfield model [12], which includes mainly two parts.
They cover the Hopfield model, a network counting chimes, associative memory networks at low rates, towards networks of spiking neurons, the Miyashita correlations, learning in networks with discrete synapses, the Behavioral and Brain Science review, dynamics of networks of spiking neurons, electronic implementations, prospective activity, multi-item working memory, learning with spike-driven plastic synapses, and familiarity recognition.
Hopfield model has been used for the estimation of tropospheric delay [4].
The computation of the ZHD may be done with the aid of the models developed so far; we mention here the Hopfield model, the Saastamoinen model, etc.
The tropospheric delay algorithm is based on the Hopfield model. The Galileo Sensor Station (GSS) is a derivative of the static user receiver model used within GSSF.
An FCM Works as a mono layer neural network similar to the Hopfield model, but with auto concurrent connections and step, sigmoid and linear activation functions.
The hydrostatic component of the troposphere can be mitigated by a number of models like the Saastamoinen or Hopfield model. One can use these models utilizing standard atmosphere parameters or by entering into model surface meteorological data from global or regional numerical meteorological models such as European Centre for Medium-Range Weather Forecast (ECMWF), the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) or Global Pressure and Temperature (GPT) (Boehm et al., 2007; Kalita et al., 2014).
The Hopfield model is used to take into account the influence of the troposphere.
This motivates our development of a computational framework, using the Hopfield model for associative memory, which can provide a platform to study the conditions in which an auxiliary external network may prevent and/or reverse TBI and neurodegenerative impairments.
Hopfield Model. In our autoassociative memory network, coupled artificial neurons respond to meaningful external queues with stable, collective activity patterns (where 1 [less than or equal to] [mu] [less than or equal to] 20).
Perceptron is aimed at physical systems and does not consider abstract population characteristics where the Hopfield model does.