Stochastic Multilevel Programming with a Hybrid Intelligent Algorithm

A framework of stochastic multilevel programming is proposed for modelling decentralized decision-making problem in stochastic environment. According to different decision criteria, the stochastic decentralized decision-making problem is formulated as expected value multilevel programming, and chanceconstrained multilevel programming. In order to solve the proposed stochastic multilevel programming models for the Stackelberg-Nash equilibriums, genetic algorithms, neural networks and stochastic simulation are integrated to produce a hybrid intelligent algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the hybrid intelligent algorithm

Some Concepts and Theorems of Uncertain Random Process

As a mixture of randomness and uncertainty, uncertain random variable is a measurable function from a probability space to a collection of uncertain variables. This paper will propose an uncertain random process as a generalization of both stochastic process and uncertain process. Special types of uncertain random process such as independent and stationary increment uncertain random process and uncertain random renewal process will also be discussed

Fuzzy multilevel programming with a hybrid intelligent algorithm

AbstractIn order to model fuzzy decentralized decision-making problem, fuzzy expected value multilevel programming and chance-constrained multilevel programming are introduced. Furthermore, fuzzy simulation, neural network, and genetic algorithm are integrated to produce a hybrid intelligent algorithm for finding the Stackelberg-Nash equilibrium. Finally, two numerical examples are provided to illustrate the effectiveness of the hybrid intelligent algorithm

Control System Design of Threshing Separator Based on ARM

In order to realize the safe operation of the amphibious multifunctional threshing separator, designing the control system based on the ARM embedded processor, the system through the CAN industrial field bus, connected with the temperature sensor, the infrared sensor, the stroke sensor, the revolution speed sensor, control the automatic clutch and the brake system through the electric relay. When the equipment overheating, personnel illegal operation, speed anomalies, equipment location errors, etc., the system can be timely control of the device brake and shut-down, to avoid serious safety accidents

Cathode Pressure Control of Air Supply System for PEMFC

This paper proposes a backstepping method controller for the polymer electrolyte membrane fuel cell (PEMFC)air supply system. The control objective is adjusting the cathode pressure to its reference value quickly, in order to solve the problem of excessive extreme pressure difference between anode and cathode in practice. Considering model uncertainty and some disturbances, we design an extend state observer (ESO) to estimate disturbances. Next, a backstepping method is proposed to adjust control law. Finally, the experiment results demonstrate the effectiveness and robustness of the control strategy

Uncertain Core for Coalitional Game with Uncertain Payoffs

Abstract Coalitional game deals with situations that involve cooperations among the players. There are different solution concepts such as core, Shapley value and nucleolus. This paper investigates uncertain coalitional game in which the payoffs are uncertain variables. Within the framework of uncertainty theory, two definitions of uncertain core are proposed. Meanwhile, a sufficient and necessary condition is provided for finding the uncertain core. Finally, an example is provided for illustrating purpose

A two-stage production planning model in fuzzy decision systems ∗

Abstract. Based on credibility theory and two-stage fuzzy optimization method, this paper presents a type of multi-product multi-period (MPMP) production planning model. Since the proposed production planning problem includes fuzzy variable coefficients with an infinite support, it is infinite-dimensional optimization problem. The approximation of the production planning problem is discussed, and the convergence of the objective value (optimal value) of the original production planning problem to the objective value (optimal value) of the approximating production planning problem is also considered. Finally, the approximation method is embedded into a genetic algorithm (GA) to produce an approximation-based GA for solving the proposed production planning problem, and a numerical example is provided to show the feasibility of the algorithm