Jan 29, 2002 · The SOR network can obtain the desired input/output relationship of a target system by using the input/output vector pairs and their evaluations ...
The effectiveness of the proposed ASOR network is verified by applying it to design of the control system of the DC motor whose load changes with time.
The SOR network can obtain the desired input/output relationship of a target system by using the input/output vector pairs and their evaluations. In order to ...
Nov 29, 2017 · In this paper, a self-organizing fuzzy neural network with adaptive gradient algorithm (SOFNN-AGA) is proposed for nonlinear systems modeling.
Missing: SOR | Show results with:SOR
In this paper, an efficient self-organizing recurrent radial basis function neural network (RRBFNN), is developed for nonlinear system modeling. In RRBFNN, a ...
Missing: SOR | Show results with:SOR
In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems.
Missing: Employing Organization Evaluation - SOR
In this paper, an approximate approach is introduced to overcome the inaccuracy and inconsistency between the linearized model and the real process, due to ...
Missing: Self- | Show results with:Self-
The SOM produces modeling results as accurate as those produced by multilayer perceptron (MLP) networks, and better than those produced by radial basis ...
In this paper, a novel self-organizing radial basis function (SORBF) neural network is proposed for nonlinear identification and modeling.
Missing: SOR | Show results with:SOR
A self-organizing recurrent fuzzy neural network based on multivariate time series analysis · Computer Science, Engineering. Neural Computing and Applications.
Missing: Employing Evaluation - SOR