Cooperative coevolution employs different problem decomposition methods to decompose the neural network training problem into subcomponents. The efficiency of a problem decomposition method is dependent on the neural network architecture and the nature of the training problem. The adaptation of problem decomposition methods has been recently proposed which showed that different problem decompos...

Several influential connectionist models of the word segmentation task (e.g. Cairns, Shillcock, Chater, & Levy, 1997; Christiansen, Allen, & Seidenberg, 1998) follow Elman (1990) in using simple recurrent networks (SRNs). The use of SRNs in this context appears to be traditional rather than independently motivated. This paper investigates whether alternatives to SRNs can achieve similar perform...

Target Papers: • William H. Wilson, A comparison of architectural alternatives for recurrent networks, Proceedings of the Fourth Australian Conference on Neural Networks, ACNN’93, Melbourne, 13 February 1993, 189-192. ftp://ftp.cse.unsw.edu.au/pub/users/billw/wilson.recurrent.ps.Z • William H. Wilson, Stability of learning in classes of recurrent and feedforward networks, in Proceedings of the ...

Landslide displacement prediction is an important part of monitoring and early warning systems. Effective instrumental in reducing the risk landslide disasters. This paper proposes a model based on variational mode decomposition genetic algorithm optimization Elman neural network (VMD–GA–Elman). First, using VMD, sequence decomposed into three subsequences trend term, periodic random term. Then...

In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNNs integrating improvements for sequence labeling, and we compare them to the more traditional Elman and Jordan RNNs. We compare all models, either traditional or new, on four distinct tasks of sequence labeling: two on Spoken Language Understanding (ATIS and M...

The aim of this work is to present a library, developed in Modelica, which provides the neural network mathematical model. This library is developed to be used to simulate a non-linear system, previously identified through a specific neural network training system. The NeuralNetwork library is developed in Modelica 2.2 and it offers all the required capabilities to create and use different kind...