Stability is an important indicator for evaluating complex dynamic systems’ performance. Many problems in practice are abstracted into the stability of networks. This study examines stochastic fuzzy Cohen-Grossberg neural networks(CGNNs) with delayed pth moment exponential and almost sure stability. It improvement supplement to existing work. Our method based on integral inequality, differentia...

This paper describes Soft Computing approach to modeling time-dependent (dynamic, real time) transportation phenomenon characterized by uncertainty. The proposed “intelligent” control systems that are based on a combination of fuzzy logic (or neural networks) and mathematical programming (or heuristic) techniques make “on line” control decisions of the highest quality. In the first step of the ...

A recurrent self-organizing neural fuzzy inference network (RSONFIN) is proposed in this paper. The RSONFIN is inherently a recurrent multilayered connectionist network for realizing the basic elements and functions of dynamic fuzzy inference, and may be considered to be constructed from a series of dynamic fuzzy rules. The temporal relations embedded in the network are built by adding some fee...

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

Recent research has shown that one can train a neural network with binary weights and activations at train time by augmenting the weights with a high-precision continuous latent variable that accumulates small changes from stochastic gradient descent. However, there is a dearth of work to explain why one can effectively capture the features in data with binary weights and activations. Our main ...

Successfully predicting missing components (entire parts or voices) from complex multipart musical textures has attracted researchers of music information retrieval and music theory. However, these applications were limited to either two-part melody and accompaniment (MA) textures or four-part Soprano-Alto-Tenor-Bass (SATB) textures. This paper proposes a robust framework applicable to both tex...

In recent decades artificial neural networks (ANNs) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. This paper presents the application of artificial neural networks to predict drought in Yazd meteorological station. In this research, different archite...

in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...

Recent advancements in feed-forward convolutional neural network architecture have unlocked the ability to effectively use ultra-deep neural networks with hundreds of layers. However, with a couple exceptions, these advancements have mostly been confined to the world of feed-forward convolutional neural networks for image recognition, and NLP tasks requiring recurrent networks have largely been...