Title :
A primal neural network for solving nonlinear equations and inequalities
Author :
Yunong Zhang ; Shuzhi Sam Ge
Author_Institution :
Dept. of Electron. & Electr. Eng., Strathclyde Univ., Glasgow
Abstract :
In this paper, the concept and utility of primal neural networks are introduced for the context of dynamical constraints or inequalities. Based on the neural-network design experience on solving linear equations/inequalities, we generalize a primal neural network to handling the nonlinear situation. Numerical examples (including the robotic applications) are given to demonstrate the effectiveness of the primal network
Keywords :
mathematics computing; neural nets; nonlinear equations; dynamical constraint; dynamical inequalities; nonlinear equations; nonlinear inequalities; primal neural network; Computer networks; Hopfield neural networks; Kinematics; Manipulators; Neural network hardware; Neural networks; Nonlinear equations; Power engineering and energy; Recurrent neural networks; Robot sensing systems;
Conference_Titel :
Cybernetics and Intelligent Systems, 2004 IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
0-7803-8643-4
DOI :
10.1109/ICCIS.2004.1460767
