๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

FLIN: Fuzzy linear interpolating network

โœ Scribed by Peter de B. Harrington; Brian W. Pack


Publisher
Elsevier Science
Year
1993
Tongue
English
Weight
717 KB
Volume
277
Category
Article
ISSN
0003-2670

No coin nor oath required. For personal study only.

โœฆ Synopsis


A fuzzy linear interpolating network (FLIN) has been developed that is a local processing neural network. Local processing advantageously furnishes a traceable mechanism of inference and a bounty of diagonlstic information in the variable scores and observation loadings of the processing units. FLIN is a two layer network for which the first layer accomplishes data driven model selection and the second layer provides linear predictive models. A new method of training is presented that enhances the relations between unsupervised and supervised layers of this network. The advantages of FLIN are demonstrated with a spectrophotometic titration of litmus.


๐Ÿ“œ SIMILAR VOLUMES


Neural network technique for fuzzy multi
โœ Mitsuo Gen; Kenichi Ida; Reiko Kobuchi ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 236 KB

Neural Network(NN) is well-known as one of powerful computing tools to solve optimization problems. Due to the massive computing unit-neurons and parallel mechanism of neural network approach we can solve the laxge-scale problem efficiently and optimal solution can be gotten. In this paper, we intor

IDENTIFICATION OF RESTORING FORCES IN NO
โœ Y.C. LIANG; D.P. FENG; J.E. COOPER ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 260 KB

The fuzzy adaptive back-propagation (FABP) algorithm which combines fuzzy theory with arti"cial neural network techniques is applied to the identi"cation of restoring forces in non-linear vibration systems. Simulated results show that the FABP algorithm is e!ective for the identi"cation of dynamic s