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...

Structure identification has been used widely in many contexts. Grammatical Learning methods are used to find structure information through sequences. Due to negative results, alternative representations have to be used for Grammatical Learning. One such representation is recurrent neural network. Recurrent neural networks are proposed as extended automata. In this chapter, we first summarize r...

We present an interpretable framework for path prediction that learns scene-specific causations behind agents’ behaviors. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide topdown view of the scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns “where to look” in the large image when solving the path prediction tas...

Second order properties of cost functions for recurrent networks are investigated. We analyze a layered fully recurrent architecture, the virtue of this architecture is that it features the conventional feedforward architecture as a special case. A detailed description of recursive computation of the full Hessian of the network cost function is provided. We discuss the possibility of invoking s...

Recurrent neural networks and hidden Markov models have been the popular tools for sequence recognition problems such as automatic speech recognition. This work investigates the combination of recurrent neural networks and hidden Markov models into the hybrid architecture. This combination is feasible due to the similarity of the architectural dynamics of the two systems. Initial experiments we...

Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated estimate GV. In this research, four convolutional neural networks (CNNs), Boltzmann machines, belief net recurrent applied. The GV 42 positioning stations is entered methods. output...

Three recurrent neural networks are presented for computing the pseudoinverses of rank-deficient matrices. The first recurrent neural network has the dynamical equation similar to the one proposed earlier for matrix inversion and is capable of Moore–Penrose inversion under the condition of zero initial states. The second recurrent neural network consists of an array of neurons corresponding to ...

This chapter presents an application of neural networks to chaos synchronization. The two main methodologies, on which the approach is based, are recurrent neural networks and inverse optimal control for nonlinear systems. On the basis of the last technique, chaos is first produced by a stable recurrent neural network; an adaptive recurrent neural controller is then developed for chaos synchron...