Song Shiji

Subgradient-based feedback neural networks for non-differentiable convex optimization problems

This paper developed the dynamic feedback neural network model to solve the convex nonlinear programming problem proposed by Leung et al. and introduced subgradient-based dynamic feedback neural networks to solve non-differentiable convex optimization problems. For unconstrained non-differentiable convex optimization problem, on the assumption that the objective function is convex coercive, we proved that with arbitrarily given initial value, the trajectory of the feedback neural network constructed by a projection subgradient converges to an asymptotically stable equilibrium point which is also an optimal solution of the primal unconstrained problem. For constrained non-differentiable convex optimization problem, on the assumption that the objective function is convex coercive and the constraint functions are convex also, the energy functions sequence and corresponding dynamic feedback subneural network models based on a projection subgradient are successively constructed respectively, the convergence theorem is then obtained and the stopping condition is given. Furthermore, the effective algorithms are designed and some simulation experiments are illustrated.

Reverse triple I method with restriction degree in fuzzy reasoning

The theory of the reverse triple I method with restriction degree is proposed by using the operator R/sub 0/, the computation formulas of infimum for fuzzy modus ponens and supremum for fuzzy modus tollens are shown respectively. General computation formulas of infimum for fuzzy modus ponens (FMP) and supremum for fuzzy modus tollens (FMT) of the /spl alpha/-reverse triple I method of restriction are obtained.

Optimal inventory control policy in fuzzy sense

This paper developed the EOQ model with fuzzy lead time. The lead time was fuzzified into tripodal fuzzy number and the fuzzy average cost was obtained. To find the optimal order quantity and reorder point, the average cost was defuzzified using signed distance in fuzzy sense. By analysing the convexity of this defuzzified function, the characterization of the optimal solution was obtained, from which one could compute the optimal solution easily. Furthermore numerical examples were provided, which show that if the uncertainty of lead time is small, the optimal average cost increases with the uncertainty of it very slowly.

ON THE PERIODIC SOLUTIONS OF DIFFERENTIAL INCLUSIONS AND APPLICATIONS

The periodic problem of evolution inclusion is studied and its results are used to establish existence theorems of periodic solutions of a class of semi-linear differential inclusion. Also existence theorem of the extreme solutions and the strong relaxation theorem are given for this class of semi-linear differential inclution. An application to some feedback control systems is discussed.

On stability of delayed cellular neural networks with sigmoid output functions

In this paper a sufficient condition is presented to ensure the complete stability of delayed CNNs. Such a condition establishes a relation between the time delay and the parameters of the networks. Specially, for a given output function f(x) = tanh(x), we address a sufficient condition to ensure absolute convergence of system state.

Modeling and optimization of ocean-going unloading problem with stochastic demand

One of the important Challenges for steel enterprises is to reduce the procuremen1t cost occurred in unloading process of ocean-going ship, ore as the main material contains usually some uncertain demand, and its unloading process is a typically complex problem with large scale. In this paper, unloading cost composition is analyzed strictly, and its mathematical model with stochastic demand is established wherein various complex constrains are considered. Further, Lagrangian relaxation algorithm and genetic algorithm combined with sub-gradient is designed to solve this problem. Finally, an example is simulated and illustrated to interpret the effectiveness and accuracy of proposed algorithm.

A model of stochastic inventory management with intellectual search method

Inventory cost model for sole product is investigated in stochastic environment. The logistics cost consists of holding cost, shortage cost and transportation cost. The stochastic lead-time of goods and stochastic arrival time of ships are simultaneously considered. Combining with the national well-known steel companys oversea purchasing processing, we firstly propose a mathematical model to formulate this complex problems. The genetic algorithm is applied to solve this problem because of its complexity. The numerical examples are presented to illustrate the validity for solving this model with stochastic uncertainty.

Multi-Population Bayesian Optimization Algorithm using Cooperated Pattern Search Strategy

This paper proposed a multi-population based Bayesian optimization algorithm (BOA). It combines the Bayesian statistic optimization technique with multi-population scheme. And the cooperated pattern search mechanism is provided, which is used to avoid premature convergence problem. It includes the pattern selection, model structure update and model parameter update processes. And a constraint planning model is used to balance the parameter update process. The results of examination test show the new algorithms efficiency.

Study on EOQ model in the fuzzy environment

To find the optimal reorder point and order quantity which minimize the total average cost, an EOQ model with fuzzy lead time was proposed. By analyzing the convexity of the objective function, the optimal solution was obtained for each /spl alpha/-cut of lead time with Kuhn-Tucher Conditions. Hence, the membership function of the optimal reorder point and order quantity were defuzzified into real number. Furthermore, examples were provided in which the lead time was characterized as a trapezoidal fuzzy number and the optimal reorder point and order quantity were resolved.