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In this paper, a practical Identification strategy is applied to the optimal design of fractional-order iterative learning control (FOILC). The initialized fractional-order gray-box system with commensurate order or non-commensurate order is identified by using the fractional-order iterative learning Identification and the least square or instrumental variable method. The optimal Dα-type FOILC is...
To deal with the problems of topological structure cannot adjust adaptively, easy to trap into the local minimum and diversity losing in traditional particle swarm optimization algorithm, a newly adaptive PSO algorithm based on dynamic link matrix was proposed, which build the neighborhoods though link matrix and divide them into the sub-swarm based on feature clustering. The algorithm can adjust...
In this paper, the performance analysis of the model of gradient neural network (or termed G-type model), which was designed originally for solving constant linear equation, is investigated, analyzed and simulated for online solution of Zhang linear equation (ZLE or termed time-varying linear equation). Compared with the constant case, G-type model for online ZLE solving can only approximately approach...
This paper solves the problem of time synchronization in wireless sensor networks (WSNs) with noise. The consensus based approach is an improved average value based protocol. This algorithm, compared with the existing consensus-based synchronization approach, has the advantage of being totally distributed, asynchronous and robust to process and measurement noise. The main idea of this algorithm is...
This paper considers the global convergence of a class of nonlinear dynamical networks, and the subsystems are discrete time pendulum-like systems. Different from most of the existing results, two kinds of interconnections are considered in view of the fact that the subsystems of networks may have more than one kind of interconnection between each other. The Kalman-Yakubovich-Popov (KYP) lemma and...
An integrated guidance and control model for missile control system design is established in this paper. Finite-time stability theory is used to design a finite-time convergent guidance and control law based on the proposed model. Some simulation tests are done to verify the validity of the control law designed in this paper and the advantages compared with other guidance laws. The results show the...
A parametric design problem of the finite time state observers for the linear time-invariant systems is investigated. The design aim of the finite time state observers in linear time-invariant systems is to present the parametric form of the state observers which can estimate the system state in a predefined finite time and the estimation stays bounded during the convergence time interval. Based on...
In this paper, we propose two modified conjugate gradient methods, which produce sufficient descent direction at every iteration. The theoretical analysis shows that the algorithms are global convergence under some suitable conditions. The numerical results show that both algorithms are efficient for the given test problems from the Matlab library.
This paper aims at the characteristics of reactive power optimization of the electric power system with the wind farm; proposing a new method for reactive power optimization on the entire power grid which uses the Parallel Immune Particle Swarm Optimization. It uses integer and real number hybrid encoding, improves the efficiency of compiling. And it combines the continuous and discrete particle swarm...
This paper develops an opposition-based learning harmony search algorithm with mutation (OLHS-M) for solving global continuous optimization problems. The proposed method is different from the original harmony search (HS) in three aspects. Firstly, opposition-based learning technique is incorporated to the process of improvisation to enlarge the algorithm search space. Then, a new modified mutation...
One of the most important features of the PSO algorithm is its fast convergence. This is a positive feature as long as there's no premature convergence. Inspired by the phenomenon of quorum sensing behavior in the bacteria, we incorporate this bio-behavior into PSO and MOPSO to maintain the swarm diversity and promote global exploration when the velocity of each particle in the swarm is rather small...
In order to improve convergence speed and precision of optimization in quantum particle swarm optimization (QPSO), an improved quantum particle swarm optimization (IQPSO) algorithm was presented. Chaotic sequences were used to initialize the origin angle position of particle, mutation operation algorithm was used to increase diversity of population and avoid premature convergence. The proposed algorithm...
In this paper, a distributed attitude consensus tracking control law is proposed for satellite formation flying by using sliding mode method. First, graph theory and Modified Rodriguez Parameters (MRPs) are introduced to describe the relation and dynamics of the team. Second, distributed terminal sliding mode observers are constructed to estimate the velocity state of rigid bodies in the team. The...
Presented in this paper is an extended version of the Multi-ADAptive LINear Element (MADALINE) neural network, termed EMADALINE, for On-line System identification of Multi-Input Multi-Output (MIMO) linear time-varying (LTV) systems Trained by Levenberg-Marquardt Method. A sliding window on the data set is used in the learning algorithm for the purpose of improving convergence speed during training...
In this paper, an iterative algorithm is established for solving a class of matrix equations with complex unknowns. By using the hierarchical identification principle, the gradient-based iterative algorithms are constructed to solve the equation AXB + CXHD = F and the coupled equations A1XB1 + A2XHB2 = F1 and C1XD1 + C2XHD2 = F2. The the convergence factor is presented to guarantee that the iterative...
By introducing the fractional-order difference into the updating formulas of the velocity and position, fractional-order particle swarm optimization algorithm is proposed. The effects on the convergence rate and accuracy are analyzed, by introducing fractional-orders in the updating formulas for the velocity and position. Moreover, the linear increasing methods to adjust the fractional-orders are...
In order to overcome the disadvantages of premature and local convergence in the traditional particle swarm optimization (PSO), an improved chaotic PSO algorithm based on adaptive inertia weight (AIWCPSO) is proposed. The initial population is generated by using chaotic mapping appropriately, in order to improve both the diversity of population and the periodicity of particles. The value of the new...
An approximate dynamic programming (ADP) based supplementary learning control method is developed to online improve the performance of existing controllers. The proposed supplementary learning structure can make full use of the prior knowledge of the pre-designed controller and endow the controller with learning ability. Moreover, by introducing the action dependent value function for policy evaluation,...
In order to improve water supply in the path optimization problems, an improved Ant Colony based on the mathematical model of water supply is put forward to figure out optimization algorithm. Using the improved ant colony algorithm research water supply pipe network optimization problems. The inspire information was normalized process and the global strategy preferred node probability select based...
To speed up convergence rate and improve local convergence in genetic algorithm, nonlinear adaptive crossover probability and mutation probability function are designed. They are based on the arctangent function with three parameters of maximal fitness, minimal fitness and average fitness. An improved adaptive genetic algorithm is proposed based on the two designed functions. Simulation results prove...
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