Sentence examples for continuous neural networks from inspiring English sources
Continuous neural networks (CNN) have been considered as a real alternative to provide models over uncertain non-parametric systems.
In this paper, the non-fragile state estimation problem is investigated for a class of continuous neural networks with time-delays and nonlinear perturbations.
Neural networks with deviating argument conjugate continuous neural networks and discrete neural networks.
Hence, this type of neural networks has the properties of both continuous neural networks and discrete neural networks.
Consider continuous neural networks with time-varying delays can be described by the following state equations: u ˙ i ( t ) = − ( c i + Δ c i ) u i ( t ) + ∑ j = 1 n ( a i j + Δ a i j ) f j ( u j ( t ) ) + ∑ j = 1 n ( b i j + Δ b i j ) f j ( u j ( t − h ( t ) ) ) + J i, (2.1).
In this study, a continuous neural network observer was designed to predict the toluene vapors elimination capacity (EC) in a fungal biofilter.
The combined solution of these problems using HNN requires the interconnection of discrete and continuous neural network models and the formulation of a unified energy function, which is quite complicated.
Based on continuous recurrent neural networks, bipolar patterns inputted from external can cause the output of neural networks to be memorized patterns.
This includes not only continuous attractor neural networks, but also discrete attractor neural networks such as Hopfield networks with graded neuronal responses [2].
A common approach used to analyze continuous attractor neural networks is to approximate the N-dimensional system of ordinary differential equations (Eq. (1)) by a partial differential equation by taking the limit as (Nrightarrowinfty).
