Title :
A neural computation for canonical representations of nonlinear functions
Author :
Huang, Qiu ; Liu, Ruey-Wen
Author_Institution :
Dept. of Electr. & Comput. Eng., Notre Dame Univ., IN, USA
Abstract :
An efficient constructive method to compute a canonical representation of any continuous nonlinear function with a neural network is presented. This neural network consists of only one hidden layer and the number of nodes in the hidden layer is estimated. A particular initial condition is chosen so that the backpropagation algorithm converges to a solution with an error between the neural network realization and the given function less than a given δ>0. Two simulation examples are used to demonstrate the method, and a simple application of this method to the design of a negative device is given
Keywords :
convergence of numerical methods; curve fitting; iterative methods; neural nets; any continuous nonlinear function; backpropagation algorithm convergence; canonical representations of nonlinear functions; constructive method; negative resistance devices design; neural computation; neural network; number of nodes; one hidden layer; simulation examples; Backpropagation algorithms; Circuits; Computer networks; Equations; Fitting; Manufacturing; Multilayer perceptrons; Neural networks; Neurons; Reliability engineering;
Conference_Titel :
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location :
New Orleans, LA
DOI :
10.1109/ISCAS.1990.112519
