Batch learning competitive associative net and its application to time series prediction

被引：0

作者：

Kurogi, S ^{[1
]}

Ueno, T ^{[1
]}

Sawa, M ^{[1
]}

机构：

[1] Kyushu Inst Technol, Dept Control Engn, Kitakyushu, Fukuoka 804, Japan

来源：

2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2004年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A batch learning method for competitive associative net called CAN2 is presented and applied to time series prediction of the CATS benchmark (for Competition on Artificial Time Series). Although we have presented online learning methods for the CAN2 so far, which are basically for infinite number of training data. Provided that only a finite number of training data are given, however, the batch learning scheme seems more suitable. We here present a batch learning method to efficiently learn a finite number of data. We finally apply the present method to the time series prediction of the CATS benchmark.

引用

页码：1591 / 1596

页数：6

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