GPN: A novel gravity model based on position and neighborhood to identify influential nodes in complex networks

The identification of influential nodes is one of the most significant and challenging research issues in network science. Many centrality indices have been established starting from topological features of networks. In this work, we propose a novel gravity model based on position and neighborhood (GPN), in which the mass of focal and neighbor nodes is redefined by the extended outspreading capability and modified k-shell iteration index, respectively. This new model comprehensively considers the position, local and path information of nodes to identify influential nodes. To test the effectiveness of GPN, a number of simulation experiments on nine real networks have been conducted with the aid of the susceptible–infected–recovered (SIR) model. The results indicate that GPN has better performance than seven popular methods. Furthermore, the proposed method has near linear time cost and thus it is suitable for large-scale networks.

Derivative free iterative methods with memory having higher R-order of convergence

We derive two iterative methods with memory for approximating a simple root of any nonlinear equation. For this purpose, we take two optimal methods without memory of order four and eight and convert them into the methods with memory without increasing any further function evaluation. These methods involve a self-accelerator (parameter) that depends upon the iteration index to increase the order of the optimal methods. Consequently, the efficiency of the new methods is considerably high as compared to the methods without memory. Some numerical examples are provided in support of the theoretical results.

INTELLIGENT CONTROLLER FOR TRACKING A 14-INCH CELESTRON TELESCOPE

The paper proposes a design of two fuzzy and Proportional-Integral-Derivative (PID) controllers for a position tracking of the 1400 Celestron telescope. The position responses; right ascension and declination in such a way that it minimizes the integral absolute error (ITAE) using a modified particle swarm optimization (MPSO). Based on the structure of the MPSO, the accelerated coefficients of the particle swarm optimization are adapted dynamically by minimizing the system error with the iteration index. The adaptive control tool combines the Fuzzy controller and MPSO to produce a powerful controller in the system (FLC-PSO). The parameters of membership functions and the PID gains are tuned simultaneously based on the MPSO, which is an efficient and simple tool for the multidimensional problem. The simulation results for both controllers are analyzed and compared on a basis of the time response specifications.

PD-type iterative learning control for linear continuous systems with arbitrary relative degree

This article investigates the iterative learning control problem for linear continuous systems with fixed initial shifts. The systems have arbitrary relative degree and PD-type learning schemes are proposed. Under the effect of the PD-type learning schemes, the output-limiting trajectory is constructed. Based on the contraction mapping method, we show that the schemes can guarantee that the output of the iterative system converges uniformly to the output-limiting trajectory on the finite-time interval as the iteration index tends to infinity. A simulation example is used to illustrate the effectiveness of the proposed method.

Novel Adaptive Bacteria Foraging Algorithms for Global Optimization

This paper presents improved versions of bacterial foraging algorithm (BFA). The chemotaxis feature of bacteria through random motion is an effective strategy for exploring the optimum point in a search area. The selection of small step size value in the bacteria motion leads to high accuracy in the solution but it offers slow convergence. On the contrary, defining a large step size in the motion provides faster convergence but the bacteria will be unable to locate the optimum point hence reducing the fitness accuracy. In order to overcome such problems, novel linear and nonlinear mathematical relationships based on the index of iteration, index of bacteria, and fitness cost are adopted which can dynamically vary the step size of bacteria movement. The proposed algorithms are tested with several unimodal and multimodal benchmark functions in comparison with the original BFA. Moreover, the application of the proposed algorithms in modelling of a twin rotor system is presented. The results show that the proposed algorithms outperform the predecessor algorithm in all test functions and acquire better model for the twin rotor system.