Title :
Approximate radial basis function neural networks (RBFNN) to learn smooth relations from noisy data
Author :
Maffezzoni, Paolo ; Gubian, Paolo
Author_Institution :
Department of Electron. Eng., Brescia Univ., Italy
Abstract :
In this paper a novel RBFNN scheme is presented introducing the idea of strongly delocalized neural receptive fields. Based on delocalization, a robust deterministic annealing procedure is proposed for determining the RBF centers. It is shown that highly overlapped receptive fields exhibit good noise rejection capability. A real world sensor application of this RBFNN is described. By approximating the exact gaussian fields with a suitable radial function (RF), the forward step of the RBFNN can be efficiently implemented in a single digital chip for real time sensor applications
Keywords :
intelligent sensors; learning (artificial intelligence); neural nets; simulated annealing; smoothing methods; data smoothing; delocalization; digital chip; gaussian fields; learning; noise rejection; radial basis function neural networks; real time sensor; receptive fields; robust deterministic annealing; Annealing; Backpropagation; Data engineering; Hardware; Neurons; Noise level; Noise robustness; Radial basis function networks; Radio frequency; Sensor phenomena and characterization;
Conference_Titel :
Circuits and Systems, 1994., Proceedings of the 37th Midwest Symposium on
Conference_Location :
Lafayette, LA
Print_ISBN :
0-7803-2428-5
DOI :
10.1109/MWSCAS.1994.519299
