A recurrent neural network is considered that can retrieve a collection of patterns, as well as slightly perturbed versions of this ‘pure’ set of patterns via fixed points of its dynamics. By replacing the set of dynamical constraints, i.e., the fixed point equations, by an extended collection of fixed-point-like equations, analytical expressions are found for the weights wij(b) of the net, whi...

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...

You want your neural net algorithm to learn sequences? Do not just use conventional gradient descent (or approximations thereof) in recurrent nets, time-delay nets etc. Instead, use your sequence learning algorithm to implement the following method: No matter what your nal goals are, train a network to predict its next input from the previous ones. Since only unpredictable inputs convey new inf...

Recurrent networks can be used as associative memories where the stored memories represent fixed points to which the dynamics of the network converges. These networks, however, also can present continuous attractors, as limit cycles and chaotic attractors. The use of these attractors in recurrent networks for the construction of associative memories is argued. Here, we provide a training algori...

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...

Time-Spatial data plays a crucial role for different fields such as traffic management. These data can be collected via devices such as surveillance sensors or tracking systems. However, how to efficiently analyze and visualize these data to capture essential embedded pattern information is becoming a big challenge today. Classic visualization approaches focus on revealing 2D and 3D spatial inf...

Abstract This chapter considers recurrent neural (RN) networks. These are special network architectures that useful for time-series modeling, e.g., applied to forecasting. We study the most popular RN networks which long short-term memory (LSTM) and gated unit (GRU) apply these mortality

In this work the task of classifying natural language sentences using recurrent neural networks is considered. The goal is the classification of the sentences as grammatical or ungrammatical. An acceptable classification percentage was achieved, using encoded natural language sentences as examples to train a recurrent neural network. This encoding is based on the linguistic theory of Government...

The prediction and modeling of dynamical systems, for example chaotic time series, with neural networks remains an interesting and challenging research problem. It seems to be rather natural to employ recurrent neural networks for which we will suggest a new structure based on the Elman net [1]. The major di erence to neural networks as proposed by Williams and Zipser [2] is the way we organize...