Title :
Application of the Hopfield network in robust estimation of parametric membership sets for linear models
Author :
Silva, Ivan N da ; De Arruda, Lucia Valeria R ; Do Amaral, W.C.
Author_Institution :
Dept. of Comput. Eng. & Ind. Autom., Univ. Estadual de Campinas, Sao Paulo, Brazil
Abstract :
High computation rates can be achieved using artificial neural networks. Optimization problems can be solved by neural networks with feedback connections by employing a massive number of simple processing elements with high degree of connectivity between these elements. In this paper, an application of Hopfield neural networks in robust parametric estimation with unknown-but-bounded disturbance is presented. The internal parameters of the Hopfield neural network are obtained using the valid-subspace technique. These parameters are explicitly computed to assure the network convergence. A comparative analysis with other robust estimation methods is carried out by a simulation example
Keywords :
Hopfield neural nets; convergence; optimisation; parameter estimation; Hopfield neural networks; connectivity; feedback connections; high computation rates; internal parameters; linear models; network convergence; optimization; parametric membership sets; robust estimation; robust parametric estimation; unknown-but-bounded disturbance; valid-subspace technique; Artificial neural networks; Computer networks; Hopfield neural networks; Intelligent networks; Least squares approximation; Noise measurement; Parameter estimation; Robustness; Uncertainty; Vectors;
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
DOI :
10.1109/ICSMC.1995.538406