Gradient Neural Dynamics Based on Modified Error Function
Abstract
The present study is devoted to methods for the numerical solution to the system of equations AXB=D. In the case certain conditions are met, the classical gradient neural network (GNN) dynamics obtains fast convergence. However, if those conditions are not satisfied, solution to the equation does not exist and therefore the error function E(t):=AV(t)B-D cannot be equal to zero, which increases the CPU time required for the calculation. In this paper, the solution to the matrix equation AXB = D is studied using the novel Gradient Neural Network (GGNN) model, termed as GGNN(A,B,D). The GGNN model is developed using a gradient of the error matrix used in the development of the GNN model. The proposed method uses a novel objective function that is guaranteed to converge to zero, thus reducing the execution time of the Simulink implementation. The GGNN-based dynamical systems for computing generalized inverses are also discussed. The conducted computational experiments have shown the applicability and advantage of the developed method.
Copyright information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Stanimirović, P. S., Gerontitis, D., Tešić, N., Kazakovtsev, V. L., Stasiuk, V., & Cao, X. (2023). Gradient Neural Dynamics Based on Modified Error Function. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 256-263). European Publisher. https://doi.org/10.15405/epct.23021.31