the structure of a self-routing interconnection network with a recurrent neural network," in Pro-Abstract A modiied Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modiied so that it has several distinct initial states. This is equivalent to a single RNN learning multiple diierent synchronous sequential mac...

The introduction of new in situ sensing creates the possibility of directly controlling critical process variables in plasma enhanced chemical vapor deposition systems (PECVD). The complexity of this process makes it necessary to develop empirical models of the system dynamics. This paper describes the experimental and numerical procedures for identifying both transfer function and recurrent ne...

This paper presents a model of a discrete-time recurrent neural network designed to perform quadratic real optimization with bound constraints. The network iteratively improves the estimate of the solution, always maintaining it inside of the feasible region. Several neuron updating rules which assure global convergence of the net to the desired minimum have been obtained. Some of them also ass...

Routing is one of the basic parts of a message passing multiprocessor system. The routing procedure has a great impact on the efficiency of a system. Neural algorithms that are currently in use for computer networks require a large number of neurons. If a specific topology of a multiprocessor network is considered, the number of neurons can be reduced. In this paper a new recurrent neural ne...

Financial forecasting is an example of a signal processing problem w hich is challenging due to Small sizes, high noise, nonstationarity, and non-linearity,but fast forecasting of stock market price is very important for strategic business planning.Present study is aimed to develop a comparative predictive model w ith Feedforward Multilayer Artif icial Neural Netw ork & Recurrent Time Delay Neu...

This paper presents an electric power monoring based on Artificial Neural Network (ANN) for the nuclear power plants. The Recurrent Neural Networks (RNN) and the feed-forward neural network are selected for the plant modeling and anomaly detection because of the high capability of modeling for dynamic behaviors. Two types of Recurrent Neural Networks (RNN) are used. The first one Elman type of ...

We describe a computational framework for language learning and parsing in which dynamical systems navigate on fractal sets. We explore the predictions of the framework in an artificial grammar task in which humans and recurrent neural networks are trained on a language with recursive structure. The results provide evidence for the claim of the dynamical systems models that grammatical systems ...

Learning structure in temporally-extended sequences is a diicult computational problem because only a fraction of the relevant information is available at any instant. Although variants of back propagation can in principle be used to nd structure in sequences, in practice they are not suuciently powerful to discover arbitrary contingencies, especially those spanning long temporal intervals or i...