[Lecture Notes in Computer Science] SOFSEM '95: Theory and Practice of Informatics Volume 1012 || What NARX networks can compute

✍ Scribed by Bartosek, Miroslav; Staudek, Jan; Wiedermann, Jirí

• Book ID: 120598907
• Publisher: Springer Berlin Heidelberg
• Year: 1995
• Weight: 431 KB
• Category: Article
• ISBN: 3540484639
• DOI: 10.1007/3-540-60609-2_5

✦ Synopsis

We prove that a class of architectures called NARX neural networks, popular in control applications and other problems, are at least as powerful as fully connected recurrent neural networks. Recent results have shown that fully connected networks are Turing equivalent. Building on those results, we prove that NARX networks are also universal computation devices. NARX networks have a limited feedback which comes only from the output neuron rather than from hidden states. There is much interest in the amount and type of recurrence to be used in recurrent neural networks. Our results pose the question of what amount of feedback or recurrence is necessary for any network to be Turing equivalent and what restrictions on feedback limit computational power.