The quadratic assignment problem (QAP) is one of the nondeterministic polynomial (NP)-hard combinatorial optimization problems. One of the heuristic algorithms for the QAP is tabu-search. Exponential tabusearch, which is one of improved versions of the tabu search, was proposed using a neural network, and it was further extended to chaotic tabu search with a chaotic neural network. Chaotic dyna...

Many difficult combinatorial optimization problems arising from science and technology are often difficult to solve exactly. Hence a great number of approximate algorithms for solving combinatorial opthintion problems have been developed [lo], [IS]. Hopfield and Tank applied the continuowtime, continuous-output Hopfield neural network (CTCGH?W) to TSP, thereby initialing a new approach to optim...

Shannon wavelet chaotic neural network is a kind of chaotic neural network with non-monotonous activation function composed by Sigmoid and Wavelet. In this paper, wavelet chaotic neural network models with different nonlinear self-feedbacks are proposed and the effects of the different self-feedbacks on simulated annealing are analyzed respectively. Then the proposed models are applied to the 1...

Abstract—This paper presented a video watermarking algorithm based on wavelet chaotic neural network. First, to enhance binary image’s security, the algorithm encrypted it with double chaotic based on Arnold and Logistic map, Then, the host video was divided into some equal frames and distilled the key frame through chaotic sequence which generated by Logistic. Meanwhile, we distilled the low f...

Chaotic neural network with external inputs has been used as a mixed input pattern separator. In contrast to previous work on the subject, highly chaotic dynamical system (LLE ≈ 0.6) is applied here. The network is based on a “dynamical mapping” scheme as an effective framework for cortical mapping. This feature allows for a more effective pattern retrieval and separation by the network.

In this paper, based on genetic algorithm (GA) and steepest descent method (SDM), we proposed a sandwich-like algorithm for the learning of neural network to identify some chaotic systems. The chaotic systems interested in this paper are the duffing equation. Different identification schemes of neural network are used to identify the duffing equation. Simulation results show that performance of...

Neural networks have been shown to be powerful tools for solving optimization problems. In this paper, we first retrospect Chen’s chaotic neural network and then propose several novel chaotic neural networks. Second, we plot the figures of the state bifurcation and the time evolution of most positive Lyapunov exponent. Third, we apply all of them to search global minima of continuous functions,...

Even though it is known that chaotic time series cannot be accurately predicted, there is a need to forecast their behavior in may decision processes. Therefore several non-linear prediction strategies have been developed, many of them based on soft computing. In this chapter we present a new neural network architecutre, called Hybrid and based-onWavelet-Reconstructions Network (HWRN), which is...

-This paper studies global bifurcation structure of the chaotic neural networks applied to solve the traveling salesman problem (TSP). The bifurcation analysis clarifies the dynamical basis of the chaotic neuro-dynamics which itinerates a variety of network states associated with possible solutions of TSP and efficiently 'searches'for the optimum or near-optimum solutions. By following the deta...

This paper presents a new approach to solve synchronization problem of a large class of discrete chaotic systems. The chaotic systems can be reformulated as an appropriate class of linear parameter varying (LPV) systems. Then, based on the LPV representation, a neural network observer-based approach is proposed to solve the synchronization problem. The simulation results show the advantages of ...