drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

With the rapid development of information technology, security images has emerged as a significant area research. This study presents an algorithm that integrates chaotic image encryption and convolutional neural network (CNN) to enhance efficiency. The applies properties randomness nonlinear mapping sequences with advanced feature extraction capabilities CNN model achieve robust encryption. Fi...

The KIII model of the chaotic dynamics of the olfactory system was designed to simulate pattern classification required for odor perception. It was evaluated by simulating the patterns of action potentials and EEG waveforms observed in electrophysiological experiments. It differs from conventional artificial neural networks in relying on a landscape of chaotic attractors for its memory system a...

A fractional-order two-neuron Hopfield neural network with delay is proposed based on the classic well-known Hopfield neural networks, and further, the complex dynamical behaviors of such a network are investigated. A great variety of interesting dynamical phenomena, including single-periodic,multiple-periodic, and chaoticmotions, are found to exist.The existence of chaotic attractors is verifi...

In this paper, we present meta-learning evolutionary arti!cial neural network (MLEANN), an automatic computational framework for the adaptive optimization of arti!cial neural networks (ANNs) wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conven...

Large ensembles of globally coupled chaotic neural networks undergo a transition to complete synchronization for high coupling intensities. The onset of this fully coherent behavior is preceded by a regime where clusters of networks with identical activity are spontaneously formed. In these regimes of coherent collective evolution the dynamics of each neural network is still chaotic. These resu...

We propose a theory of deterministic chaos for discrete systems, based on their representations in symbolic history spaces Ω = (B ∞ , T ∆). These are spaces of semi-infinite sequences, as the one-sided shift spaces, but endowed with a more general topology T ∆ which we call a semicausal topology. We show that Ω is a metrizable Cantor set which embeds the chaotic attractor Λ. We discuss metrical...

In neural circuits, statistical connectivity rules strongly depend on cell-type identity. We study dynamics of neural networks with cell-type-specific connectivity by extending the dynamic mean-field method and find that these networks exhibit a phase transition between silent and chaotic activity. By analyzing the locus of this transition, we derive a new result in random matrix theory: the sp...

Steganography is an art of hiding the information without any change in the external appearance of the cover object. Cryptography is a technique to make the information unreadable for unauthorized users. Making the data unreadable and hiding it, will make the data highly secure. Image steganography allows the user to hide a large amount of data inside an image. On transmission side Steganograph...

In this paper we report the result of a Monte Carlo study on the probability of chaos in large dynamical systems. We use neural networks as the basis functions for the system dynamics and choose parameter values for the networks randomly. Our results show that as the dimension of the system and the complexity of the network increase, the probability of chaotic dynamics increases to 100%. Since ...