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In this paper, constructing the improved chaotic map which multiplies the output value of the chaotic map by a large value, and subtracts its integer part. Simulation results show that the chaos range of the improved chaotic map is enlarged. The generated chaotic sequence has strong randomness. A double chaotic image encryption algorithm is proposed by combining the improved chaotic maps with the permutation and diffusion encryption structure. The algorithm can reduce the complexity while ensuring the encryption effect. The simulation results show that the encryption algorithm can resist statistical attack and has excellent robustness, and has a good development prospect in information security.

Chaotic Neural Network has the dynamics property of gradient descent and chaotic at the same time by introducing self-feedback item and simulated annealing strategy. The trigonometric function disturbance was introduced into chaotic neural network and chaotic neural units with function disturbance were studied. The reversed bifurcation and the maximum Lyapunov exponent were given and the dynamic system was analyzed. Energy function was constructed by applying the unified framework theory and was proven to be asymptotically stable. The wavelet chaotic neural network was applied in the function and combinatorial optimization problems. The simulation results show that the chaotic neural network with disturbance of trigonometric function can solve function and combinatorial optimization problems effectively.

With the development of economy, logistics becomes more and more important. Logistics efficiency depends on the structure of logistics networks. This paper briefly review the studies about complex networks from perspectives of statistical properties, structural models and application areas. Logistics networks and complex networks have same behavior pattern, so we can apply the theories of complex networks to the research of logistics networks. We can analyze the internal mechanism of logistics networks with complex networks. We can study the relationship between local interaction of logistics network nodes and robustness of networks, adaptability of networks, global evolution.

Chaotic neural networks have been proved to be strong tools to solve the optimization problems. In order to escape the local minima, a new function is introduced into the self-feedback item of the transiently chaotic neural network, this paper proposes chaotic neural network with nonlinear function self-feedback by improving the self-feedback item of Chen and Aihara's network making that the network has new character which is different from linear self-feedback network. The figures of the reversed bifurcation and the maximal Lyapunov exponents of single neural unit were given, We constructed the energy function of chaotic neural network, and analyzed the sufficient condition for the networks to reach asymptotical stability and set the parameters of the networks for solving traveling salesman problem (TSP). The simulation results in solving 10-city TSP show that the new model is valid in solving optimization problems.

This paper is concerned with the asymptotic behavior of a p-Ginzburg–Landau functional with radial structure as parameter goes to zero in the case of p ≠ 2 . By analyzing the functional globally, we show that the singularity of p-Ginzburg–Landau energy concentrates on the origin. By the fact the singularity can be balanced by some infinitesimal weight, we prove that an energy with a proper weight is globally bounded.

Chaotic neural networks can acquire the ability to escape local minima of energy functions by chaotic dynamics. A Novel Chaotic neural network model with nonlinear function self-feedback is proposed by introducing nonlinear function into self-feedback of chaotic neural network. The analyses of the optimization mechanism of the networks suggest that nonlinear function self-feedback affects the original Hopfield energy function in the manner of the sum of the multiplications of nonlinear function to the state, avoiding the network being trapped into the local minima. We constructed the energy function of chaotic neural network, and analyzed the sufficient condition for the networks to reach asymptotical stability and set the parameter set of the networks for solving traveling salesman problem (TSP). Simulation results indicate that the novel chaotic neural networks can find the optimal solution of combinatorial optimization problems.

First, analyzed the characteristics of the retailer-dominant supply chain, then based on agent method, established a simulation model of three level supply chain aiming at the problem of demand information sharing and a detailed description of the modeling process was given. Finally, use the simulation model to analyze the influence of information sharing in the retailer-dominated supply chain and all the members. Provided a new research method could solve the coordination problem of enterprises in retailer-dominant supply chain.

Transient chaotic neural network (TCNN) with chaotic simulated annealing (CSA), which can control the state searching in a fractal region, has a higher searching efficiency. However, the TCNN is not guaranteed to find the globally optimal solutions because CSA has completely deterministic dynamics. Contrary to CSA, stochastic simulated annealing (SSA) can search a globally optimal solution with probability one if the annealing speed is sufficiently slow. The white noise is one of the most common stochastic model and used widely in the engineering. In order to retain the excellent optimization property of SSA, the white noise is proposed into the TCNN. And the improved neural network is applied in the broadcast scheduling problems (BSP). The simulation results show that as long as we control the white noise in a proper range, we will be more likely to find an optimal or near-optimal time-division multiple-access (TDMA) frame structure with a minimal average delay time.

Coordination mechanism based on the three levels of supply chain consists of a single vendor, a single manufacturer and a single seller. Three levels of supply chain are used firstly for the coordination of supply chain by quantitative method. The strategies to fix price are discussed using Game theory, as a single vendor, a single manufacturer and a single sellers are in the cooperation of the independent decision-making and cooperation of the collective decisions. The interests of two strategies for the enterprises are also involved. The result shows that the integrated interest of the supply chain under the cooperation case is larger than that in the supply chain of not cooperation case. The supply chain node cooperation between enterprises is superior than not cooperation in enterprises own interests, as distributive factors must be taken from some range. Core businesses could choose appropriate distributive factors in order to reach the harmony of interest and get the overall stability of high interest, which depends on the power of bargaining.

The Small and Medium Enterprises (SMEs) in the national economy play an irreplaceable role in solving the employment and achieving social stability. The credit is discriminative and the financing is difficult. The paper takes into account the interests of all parties, which is conducive to share the benefits. Supply chain financing addresses the difficulties of the small and medium enterprises financing, which provides an important way. The risks are researched in the process of implementation of supply chain financing models and preventive measures are given. Supply chain financing can have a huge space for development.

A cooperation model for the balance of one-shot cooperation in the two-echelon supply chain was derived following the principal-agents mechanism with the factors influencing profit distribution being known based on of supply chain coordination model. The results of analysis showed that the effort level is lower when a noncooperative strategy is adopted than when a cooperative strategy is adopted. At the same time, the optimal effort level and distribution coefficient were got by using of the model.

A Chaotic neural network model with anti-trigonometric function self-feedback is proposed by introducing anti-trigonometric function into self-feedback of chaotic neural network The analyses of the optimization mechanism of the networks suggest that anti-trigonometric function self-feedback affects the original Hopfield energy function in the manner of the sum of the multiplications of anti-trigonometric 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 function optimization and TSP indicates that the proposed networks can find the optimal solution of combinatorial optimization problems.

In the practical applications and hardware realization of chaotic neural networks, because of the instability of hardware components and impacts of the surrounding environment, the system is subject to noise interference inevitably. In this paper, in order to study the effect of white noises on chaotic neural network, the optimization performance of chaotic neural network interrupted by white noises is deeply researched and analyzed. The simulation result of chaotic neural network interrupted by white noises illustrates the influence of white noises on the system. The simulation results show that the system still has good optimization ability under the white noises with suitable signal to noise.

A chaotic neural network with self-feedback of trigonometric function is presented by introducing non-linear trigonometric function as self-feedback of chaotic neural network. The reversed bifurcation and the maximum Lyapunov exponent of the chaotic neuron are given. This chaotic neural network is used to the 10-city traveling salesman problem (TSP), and the influence of trigonometric function self-feedback on TSP is analyzed. The simulation on TSP indicates that the chaotic neural network with self-feedback of trigonometric function can realize the global optimization with faster speed and that the optimization performance of the proposed chaotic neural network is superior to that of the network with linear self-feedback.

Wavelet chaotic neural networks have successfully solved function and combinatorial optimization problems. Gauss wavelet chaotic neural units with the annealing function of subparagraph index were studied. The reversed bifurcation and Lyapunov exponent figures were respectively given. On the basis of Gauss wavelet chaotic neural network, the annealing function of subparagraph index was introduced into network, a new reformative wavelet chaotic neural network was presented. Then it was applied to function and combinatorial optimization problems. The simulation results show that the search-optimization capacity of wavelet chaotic neural network has been improved and the reformative wavelet chaotic neural network is superior to the primary wavelet chaotic neural networks.

The fuzzy integral equations are important part of the fuzzy analysis theory. The integro-integral equations are a class of important integral equations, which come from the problems on high-order differential equations or some physics, mechanics, medicine or control engineering problems. This paper studies solvability to a class of new fuzzy integro-integral equations and gives some new results.

The paper starts from the analysis of the key elements of control on information technology investment, and continues with the analysis of chaos synchronism between information investment systems and corresponding systems of enterprise development goals elements by utilizing the synchronism method of chaos. It has set up a P-C chaos synchronism model for information technology investment and enterprise development. It also puts forward a control strategy of enterprise information technology investment.