Observer design for nonlinear systems is very important in state-based stabilization, fault detection, chaos synchronization and secret communication. This paper deals with synchronization problem of a class of fractional-order neural networks (FONNs) based on system observer. Two sufficient conditions are given for the FONNs with known constant parameters and unknown time-varying parameters, respectively. Based on the fractional Lyapunov stability criterion, the proposed sliding mode observer can guarantee that the synchronization error between two identical FONNs converges to zero asymptotically, and all involved signals keep bounded. Finally, some simulation examples are provided to indicate the effectiveness of the proposed method.
Artificial lateral line has been drawing an increasing attention recently for its potential applications in robotics. Experiments are usually conducted with a bioinspired robot in a controlled environment, where the sensing platform is held stationary or slowly driven with a simple linear motion. In this paper, we conduct a more practical and challenging study where the robot uses artificial lateral line to evaluate its linear velocity while freely swimming. We use onboard artificial lateral line to measure the pressure profiles over the surface of a robotic fish and employ onboard IMU (inertial measurement unit) to record the motion kinematics of the robot while freely swimming at various speeds. We find that 1) pressure changes are greatest on the head of the robot; 2) pressures increase along with the swimming speed and the oscillation amplitude of angular velocity of the robot. Therefore, we propose a nonlinear prediction model which incorporates distributed pressure and angular velocity to estimate the speed of the robot. Online speed evaluation experiment demonstrates the effectiveness and the accuracy of the proposed model.
Solving the constraint satisfaction problem (CSP) is to find an assignment of values to variables that satisfies a set of constraints. Ant colony optimization (ACO) is an efficient algorithm for solving CSPs. However, the existing ACO-based algorithms suffer from the constructed assignment with high cost. To improve the solution quality of ACO for solving CSPs, an ant colony optimization based on information entropy (ACOE) is proposed in this paper. The proposed algorithm can automatically call a crossover-based local search according to real-time information entropy. We first describe ACOE for solving CSPs and show how it constructs assignments. Then, we use a ranking-based strategy to update the pheromone, which weights the pheromone according to the rank of these ants. Furthermore, we introduce the crossover-based local search that uses a crossover operation to optimize the current best assignment. Finally, we compare ACOE with seven algorithms on binary CSPs. The experimental results revealed that our method outperformed the other compared algorithms in terms of the cost comparison, data distribution, convergence performance, and hypothesis test.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.