The minimum vertex cover (MVC) problem is a classic graph optimization problem. It is well known that it is an NP-Complete problem. By analyzing the dynamic behavior of the transiently chaotic neural network and the characteristics of the minimum vertex cover problem, we propose a specialized transiently chaotic neural network for this problem. Extensive simulation results show that the transie...

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series are introduced. In addition, multi neural network for load forecast including BP artificial neural network, RBF neural network and wavelet neural network are introduced, too. Then, a combination load forecasting model for power load based on chaotic time series,...

The chaotic neural network constructed with chaotic neurons exhibits rich dynamic behaviour with a nonperiodic associative memory. In the chaotic neural network, however, it is difficult to distinguish the stored patterns in the output patterns because of the chaotic state of the network. In order to apply the nonperiodic associative memory into information search, pattern recognition etc. it i...

this paper addresses a nonlinear observer based control scheme to synchronize chaotic systems subject to uncertainties and external disturbances. it is assumed that the dynamic of slave system is not completely known. in order to compensate for the system perturbation resulting from parameter variations and mismodeling phenomena, an adaptive neural network observer is employed to handle this pr...

Both the stochastic chaotic simulated annealing and the deterministic chaotic simulated annealing are used to restore gray level images degraded by a known shift-invariant blur function and additive noise. The neural networks are modeled to represent the image whose gray level function is the simple sum of the neuron state variables. The restoration consists of two stages: parameter estimation ...

A sigmoid function is necessary for creation a chaotic neural network (CNN). In this paper, a new function for CNN is proposed that it can increase the speed of convergence. In the proposed method, we use a novel signal for controlling chaos. Both the theory analysis and computer simulation results show that the performance of CNN can be improved remarkably by using our method. By means of this...

In this Letter, a novel approach of encryption based on chaotic Hopfield neural networks with time varying delay is proposed. We use the chaotic neural network to generate binary sequences which will be used for masking plaintext. The plaintext is masked by switching of chaotic neural network maps and permutation of generated binary sequences. Simulation results were given to show the feasibili...

In this letter we unveil the existence of transient hidden coexisting chaotic attractors, in a simplified Hopfield neural network with three neurons. keyword Hopfield neural network; Transient hidden chaotic attractor; Limit cycle

RNA secondary structure prediction is a computationally feasible and broadly studied problem. It can be considered as the combinatorial optimization problem. In this paper, we propose an improved transiently chaotic neural network (TCNN) for RNA secondary structure prediction. In the improved model, a variable p(t) called the acceptance probability of chaos is introduced into the original TCNN ...