Language Inference Using Elman Networks with Evolutionary Training

被引：0

作者：

Anastasopoulos, Nikolaos ^{[1
]}

Tsoulos, Ioannis G. ^{[2
]}

Dermatas, Evangelos ^{[1
]}

Karvounis, Evangelos ^{[2
]}

机构：

[1] Univ Patras, Dept Elect & Comp Engn, Patras 26504, Greece

[2] Univ Ioannina, Dept Informat & Telecommun, Arta 47100, Greece

来源：

SIGNALS | 2022年 / 3卷 / 03期

关键词：

recurrent neural networks; genetic algorithms; machine learning; RECURRENT NEURAL-NETWORKS;

D O I：

10.3390/signals3030037

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a novel Elman-type recurrent neural network (RNN) is presented for the binary classification of arbitrary symbol sequences, and a novel training method, including both evolutionary and local search methods, is evaluated using sequence databases from a wide range of scientific areas. An efficient, publicly available, software tool is implemented in C++, accelerating significantly (more than 40 times) the RNN weights estimation process using both simd and multi-thread technology. The experimental results, in all databases, with the hybrid training method show improvements in a range of 2% to 25% compared with the standard genetic algorithm.

引用

页码：611 / 619

页数：9

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