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...

The extinction of the tortoises is a critical problem on global scale. The most useful technique to assist their population growth was identified in the rescue of the eggs in order to protect the hatchlings during their first year of life. We propose an autonomous system based on input delay neural network that can automatically recognize the activity of the tortoise by analyzing signals record...

Kalman filter based training algorithms for recurrent neural networks provide a clever alternative to the standard backpropagation in time. However, these algorithms do not take into account the optimization of the hidden state variables of the recurrent network. In addition, their formulation requires Jacobian evaluations over the entire network, adding to their computational complexity. In th...

| Recurrent neural networks are suitable for solving problems with temporal extent e.g. speech recognition, time series prediction, sequence generation. The biggest problem is, however, its computational complexity during the training process. Using the well-known Real Time Recurrent Learning rule by D. Zipser and R.J. William 2], the training time of each epoch is of order O(n 4) where n is th...

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...

Multielectrode recordings have revealed zero time lag synchronization among remote cerebral cortical areas. However, the axonal conduction delays among such distant regions can amount to several tens of milliseconds. It is still unclear which mechanism is giving rise to isochronous discharge of widely distributed neurons, despite such latencies. Here, we investigate the synchronization properti...

In this paper we present a recurrent neural network model to recognize efficient Decision Making Units(DMUs) in Data Envelopment Analysis(DEA). The proposed neural network model is derived from an unconstrained minimization problem. In theoretical aspect, it is shown that the proposed neural network is stable in the sense of lyapunov and globally convergent. The proposed model has a single-laye...

This paper presents an adaptive neural control design for a class of unknown nonlinear systems. Novel state variables and the corresponding transform are introduced, such that the statefeedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a neural network observer is proposed to obtain the future sy...