Finite-Time Stability Analysis and Control for a Class of Stochastic Singular Biological Economic Systems Based on T-S Fuzzy Model

This paper studies the problem of finite-time stability and control for a class of stochastic singular biological economic systems. It shows that such systems exhibit the distinct dynamic behavior when the economic profit is a variable rather than a constant. Firstly, the stochastic singular biological economic systems are established as fuzzy models based on T-S fuzzy control approach. These models are described by stochastic singular T-S fuzzy systems. Then, novel sufficient conditions of finite-time stability are obtained for the stochastic singular biological economic systems, and the state feedback controller is designed so that the population (state of the systems) can be driven to the bounded range by the management of the open resource. Finally, by using Matlab software, numerical examples are given to illustrate the effectiveness of the obtained results

Event-triggered control for stochastic singular systems with state delay

This paper is concerned with the problem of event-triggered control for stochastic singular systems with stochastic disturbance and state delay. Based on event-triggered scheme, a model of stochastic singular closed-loop system is proposed. By employing an auxiliary vector function and an integral inequality, and then utilizing the free-weighting-matrix approach, a set of sufficient conditions is derived, which can guarantee that the considered stochastic singular system is stochastically admissible in the mean square. Furthermore, the co-design method of corresponding controller and event-triggered condition is also developed. The proposed method emphasizes the implementation of event-triggered control to stochastic singular systems with time delay. Finally, a simulation example is given to demonstrate the effectiveness and the benefits of our proposed method

Improved Artificial Bee Colony Algorithm with Adaptive Parameter for Numerical Optimization

The problem that ABC (Artificial Bee Colony) algorithm is good at exploration but poor at exploitation for the numerical optimization is investigated in this paper. PA-ABC (Parameter Adaptive ABC) algorithm is proposed, which adopts different search equations with different search abilities for the employed bee and the onlooker bee. Firstly, the best-so-far solution is introduced into each search equation to enhance exploitation; secondly, the employed bee uses two random solutions to search, so as to keep high ability of exploration; thirdly, the onlooker bee searches around a random solution to keep population diversity; most importantly, adaptive parameter computed by fitness function is introduced in the search equation of the onlooker bee, which makes the search step adjust according to the search process. So the search equation of the employed bee has balanced abilities of exploration and exploitation, while the search equation of the onlooker bee can make the search focus transfer from exploration to exploitation adaptively. The experiment results on benchmark functions show that the search performance of PA-ABC is higher than or at least comparable to basic ABC and typical improved ABCs. In addition, compared to the performance of the state-of-the-art ABC variants under their original parameter configuration, PA-ABC is verified to have similar performance to them