In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

Call admission control algorithms that reduce dropped calls in CDMA cellular systems are discussed in this paper. The capacity of a CDMA system is confined by interference of users from both inside and outside of the target cell. Earlier algorithms for call admission control is based on the effective traffic load for the target cell if one call is accepted. These algorithms ignore the interfere...

Recurrent neural network (RNN) is recognized as a powerful language model (LM). We investigate deeper into its performance portfolio, which performs well on frequent grammatical patterns but much less so on less frequent terms. Such portfolio is expected and desirable in applications like autocomplete, but is less useful in social content analysis where many creative, unexpected usages occur (e...

Abstract. In this paper we show that programming languages can be translated on recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and subsymbolic computation. To be of some use, it should be carried in a context of bo...

In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and subsymbolic computation. To be of some use, it should be carried in a context of bounded re...

In this article, we propose DeepSLAM, a novel unsupervised deep learning based visual simultaneous localization and mapping (SLAM) system. The DeepSLAM training is fully since it only requires stereo imagery instead of annotating ground-truth poses. Its testing takes monocular image sequence as the input. Therefore, SLAM paradigm. consists several essential components, including Mapping-Net, Tr...