Growing hyperspheres neural classifier and its utilization for an economical analysis

Title:

Growing hyperspheres neural classifier and its utilization for an economical analysis

Creator:

Jiřina, Marcel and Cahlík, Tomáš

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:e0b3c483-92c5-48ff-ae2c-035cb0e55115
uuid:e0b3c483-92c5-48ff-ae2c-035cb0e55115

Subject:

neural network classifier, economical analysis, and peak detection

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This article introduces an improved growing hyperspheres (GHS) neural classifier that is based on a proper distribution of hyperspheres over patterns to properly cover all the patterns of a given class. The union of these hyperspheres then form a discrimination surface among the classes. The article describes a complete general algorithrn together with all up-to-date modifications and shows its abilities on an economical problém. A comparison with results obtained by the inultilayer perceptron (MLP) neural network is presented. The problém consists in a detection of peaks (steep time changes) in a time seqiience of the total factor productivity - a residual factor in the production fmiction. The peaks can be interpreted - at least in the Real Business Cycles (RBC) Theory - as shocks caused by sudden technological innovations. The results from the GHS and MLP neural network are compared with results obtained by means of empirical rules compiled by an economic expert.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2002 Volume:12 | Number:3

Harvested from:

CDK

Metadata only:

false