This paper presents a new two-layer recurrent neural network (RNN) for a power system stabilizer (PSS) design called the recurrent neural network power system stabilizer (RNNPSS). The RNNPSS consists of a recurrent neural network identifier (RNNI) that tracks and identifies the power generator and a recurrent neural network controller (RNNC) that supplies an adaptive signal to the governor and ...

This paper presents an improvement of hybrid of nonlinear autoregressive with exogenous input (NARX) and autoregressive moving average (ARMA) for long-term machine state forecasting based on vibration data. In this study, vibration data is considered as a combination of two components which are deterministic data and error. The deterministic component may describe the degradation index of machi...

In this paper, we present FPGA recurrent neural network systems with learning capability using the simultaneous perturbation learning rule. In the neural network systems, outputs and internal values are represented by pulse train. That is, analog recurrent neural networks with pulse frequency representation are considered. The pulse density representation and the simultaneous perturbation enabl...

We propose a neural network model for coordination boundary detection. Our method relies on two common properties — similarity and replaceability in conjuncts — in order to detect both similar and dissimilar pairs of conjuncts. The model improves the identification of clause-level coordination using bidirectional recurrent neural networks incorporating two properties as features. We show that o...

In this paper, a recurrent neural network called the dual neural network is proposed for online redundancy resolution of kinematically redundant manipulators. Physical constraints such as joint limits and joint velocity limits, together with the drift-free criterion as a secondary task, are incorporated into the problem formulation of redundancy resolution. Compared to other recurrent neural ne...

During anaerobic growth, nitrate induces synthesis of the anaerobic respiratory enzymes formate dehydrogenase-N and nitrate reductase. This induction is mediated by a transcription activator, the narL gene product. The narX gene product may be involved in sensing nitrate and phosphorylating NARL. We isolated narX mutants, designated narX*, that caused nitrate-independent expression of the forma...

In the context of sequence processing, we study the relationship between single-layer feedforward neural networks, that have simultaneous access to all items composing a sequence, and single-layer recurrent neural networks which access information one step at a time. We treat both linear and nonlinear networks, describing a constructive procedure, based on linear autoencoders for sequences, tha...

This paper presents an improvement for an artificial neural network paradigm that has shown a significant potential for successful application to a class of optimization problems in structural engineering. The artificial neural network paradigm includes algorithms that belong to the class of single-layer, relaxationtype recurrent neural networks. The suggested improvement enhances the convergen...

This paper presents a study on computational promise of Simultaneous Recurrent Networks to solve large-scale optimization problems. Specifically the performance of the network for solving Traveling Salesman Problem is addressed and analyzed. A recurrent and trainable neural network, Simultaneous Recurrent Network, with Recurrent Backpropagation training algorithm is employed to address difficul...

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently, Artificial Neural Networks (ANNs) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANNs to foreca...