A Chaotic neural network model with sigmoid function self-feedback is proposed by introducing sigmoid function into self-feedback of chaotic neural network The analyses of the optimization mechanism of the networks suggests that sigmoid function self-feedback affects the original Hopfield energy function in the manner of the sum of the multiplications of sigmoid function to the state, avoiding the network being trapped into the local minima. The energy function is constructed, and the sufficient condition for the networks to reach asymptotical stability is analyzed and is used to instruct the parameter set of the networks for solving traveling salesman problem (TSP). Simulation research on functions' optimization and TSP indicates that the proposed networks can find the optimal solution of combinatorial optimization problems.
Chaotic neural networks have been applied to solve function optimization problems successfully. To improve the optimization capacity of the chaotic neural network, a new chaotic neural network model called wavelet chaotic neural network was presented by transferring sigmoid function to wavelet function. The reversed bifurcation figures of signal neural unit were given and the parameters of the new model were discussed. The wavelet function is a non-monotonic function, so the new model can spend the less time than the common chaotic neural network model in function optimization. The simulation result shows that the new chaotic neural network model is superior to the common neural network model.
Since the end of the Cold War, the study of European defence has been dominated by a ‘Common Security and Defence Policy (CSDP)-centric’ approach, while largely neglecting the comparative analysis ...