Yang Tang

On the exponential synchronization of stochastic jumping chaotic neural networks with mixed delays and sector-bounded non-linearities

This paper is concerned with the problem of exponential synchronization for stochastic jumping chaotic neural networks (SJCNNs) with mixed delays and sector non-linearities. Based on Lyapunov-Krasovskii functional and free-weighting matrix method, a delay-dependent feedback controller with sector non-linearities is proposed to achieve the synchronization in mean square in terms of linear matrix inequalities (LMIs). The activation functions are assumed to be of more general descriptions. Finally, the corresponding simulation results show the effectiveness of the proposed criteria.

Efficient multi-sequence memory with controllable steady-state period and high sequence storage capacity

Sequential information processing, for instance the sequence memory, plays an important role on many functions of brain. In this paper, multi-sequence memory with controllable steady-state period and high sequence storage capacity is proposed. By introducing a novel exponential kernel sampling function and the sampling interval parameter, the steady-state period can be controlled, and the steady-state time steps are equal to the sampling interval parameter. Furthermore, we explained this phenomenon theoretically. Ascribing to the nonlinear function constitution for local field, the conventional Hebbian learning rule with linear outer product method can be improved. Simulation results show that neural network with nonlinear function constitution can effectively increase sequence storage capacity.

Synchronization of Takagi-Sugeno Fuzzy Stochastic Complex Networks with Mixed Delays

In this paper, we propose and investigate a modelof fuzzy stochastic complex networks described by Takagi-Sugeno (T-S) fuzzy model with discrete and distributed time delays. By utilizing a new Lyapunov functional form, we employ the stochastic analysis techniques and Kronecker product to develop delay-dependent synchronization criteria that ensure the mean-square synchronization of the addressed T-S fuzzy delayed complex networks with stochastic disturbances.

ADAPTIVE SYNCHRONIZATION FOR UNKNOWN STOCHASTIC CHAOTIC NEURAL NETWORKS WITH MIXED TIME-DELAYS BY OUTPUT COUPLING

This paper is concerned with the synchronization problem for a class of stochastic neural networks with unknown parameters and mixed time-delays via output coupling. The mixed time-delays comprise the time-varying delay and distributed delay, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. Firstly, we use Lyapunov functions to establish general theoretical conditions for designing the output coupling matrix. Secondly, by using the adaptive feedback technique, a simple, analytical and rigorous approach is proposed to synchronize the stochastic neural networks with unknown parameters and mixed time-delays. Finally, numerical simulation results are given to show the effectiveness of the proposed method.

SYNCHRONIZATION IN AN ARRAY OF HYBRID COUPLED NEURAL NETWORKS WITH MODE-DEPENDENT MIXED DELAYS AND MARKOVIAN SWITCHING

In this letter, a general model of an array of N linearly coupled chaotic neural networks with hybrid coupling is proposed, which is composed of constant coupling, time-varying delay coupling and distributed delay coupling. The complex network jumps from one mode to another according to a Markovian chain with known transition probability. Both the coupling time-varying delays and the coupling distributed delays terms are mode-dependent. By the adaptive feedback technique, several sufficient criteria have been proposed to ensure the synchronization in an array of jump chaotic neural networks with mode-dependent hybrid coupling and mixed delays in mean square. Finally, numerical simulations illustrated by mode switching between two complex networks of different structure dependent on mode switching verify the effectiveness of the proposed results.

Adaptive lag generalized projective stochastic perturbation synchronization for unknown chaotic systems with different order

In this Letter, two novel types of synchronization called lag generalized projective stochastic perturbation synchronization (LGPSS) for chaotic systems with different order are introduced. The problems of reduced order (RLGPSS) and increased order LGPSS (ILGPSS) for different chaotic systems with fully unknown parameters are considered in detail. By combining the adaptive control method and feedback control technique, the suitable controllers and parameters update laws are derived to achieve RLGPSS and ILGPSS of chaotic systems. Moreover, the unknown parameters can be efficiently estimated according to a rigorous and systematic scheme. The corresponding simulation results are given to verify the effectiveness of the proposed methods.

Multi-sequence memory with dynamic synapses and controllable steady-state period

Sequential information processing, for instance the sequence memory, plays an important role on many functions of brain. In the workings of the brain, the steady-state period is alterable. But in the existing sequence memory models using hetero-associations, the steady-state period is changeless in the sequence recall. In this paper, multi-sequence memory with controllable steady-state period is proposed. By introducing a novel exponential kernel sampling function and the sampling interval parameter, the steady-state period can be controlled, and the steady-state time steps is equal to the sampling interval parameter. Owing to introducing dynamic synapses into the sequence memory, we find that the sequence storage capacity can be enlarged.

Multi-Sequence Memory with Controllable Steady-State Period

Sequential information processing, for instance the sequence memory, plays an important role on many functions of brain. In the workings of the brain, the steady-state period is alterable. But in the existing sequence memory models using hetero-associations, the steady-state period is changeless in the sequence recall. In this paper, multi-sequence memory with controllable steady-state period is proposed. By introducing a novel exponential kernel sampling function and the sampling interval parameter, the steady-state period can be controlled, and the steady-state time steps is equal to the sampling interval parameter. Furthermore, we explained this phenomenon theoretically.

Lag projective stochastic perturbation synchronization of chaotic systems

In this letter, the projective synchronization for two different chaotic systems with unknown parameters is studied. The system under consideration is subjected to delayed state and stochastic perturbation. By combining the adaptive control method and feedback control technique, we design a suitable nonlinear controller, and derive parameters update law to achieve lag projective stochastic perturbation synchronization of chaotic systems. Moreover, the unknown parameters can be efficiently estimated according to the rigorous and systematic scheme. The corresponding simulation results are given to verify the effectiveness of the proposed method.