Time series prediction using chaotic neural networks: Case study of IJCNN CATS benchmark test

被引：0

作者：

Kozma, R ^{[1
]}

Behaev, I ^{[1
]}

机构：

[1] Univ Memphis, Dept Math Sci, Comp NeuroDynam Lab, Memphis, TN 38152 USA

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：

KIII is a strongly biologically inspired neural network model. It has a multi-layer architecture with excitatory and inhibitory neurons, which have massive lateral, feedforward, and delayed feedback connections between layers. KIII has been shown previously to be an efficient tool of classification and pattern recognition. In this work we develop a methodology to use KIII for multi-step time series prediction. The method is applied for the IJCNN CATS benchmark data.

引用

页码：1609 / 1613

页数：5

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