Based on weighted signal covariance (WSC) matrix and maximum likelihood (ML) estimation, a directionof-arrival (DOA) estimation method of multiple moving targets is designed and named as WSC-ML in the presence of impulse noise. In order to overcome the shortcoming of the multidimensional search cost of maximum likelihood estimation, a novel continuous quantum particle swarm optimization (QPSO) ...

Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping a...

Quantum-behaved particle swarm optimization (QPSO), a global optimization method, is a combination of particle swarm optimization (PSO) and quantum mechanics. It has a great performance in the aspects of search ability, convergence speed, solution accuracy and solving robustness. However, the traditional QPSO still cannot guarantee the finding of global optimum with probability 1 when the numbe...

Accurate control of excavator trajectory is the foundation for intelligent and unmanned development excavators. The operation process requires multiple actuators to cooperate complete response action. However, existing methods realize a single actuator can no longer meet practical demand. Based on this, hybrid adaptive quantum particle swarm optimization algorithm (HAQPSO) proposed tune proport...

Aiming at the parameter optimization of least square support vector machine (LS-SVM), an improved quantum-behaved particle swarm optimization (IQPSO) algorithm for LS-SVM parameter selection was proposed. Based on QPSO, the algorithm optimizes particle initializing positions and improves solving speed and precision by sampling and linearizing methods. IQPSO LSSVM model was test by test function...

Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which has been applied widely for continuous optimization problems. In this paper, we propose an improved quantum-behaved particle swarm optimization with memory according to the means of best position of particles and using sigal step seaching strategy for sovle the multidimentional prob...

Abstract Quantum-behaved particle swarm optimization algorithm is analyzed in this paper, and the path of mobile robot is planned based on QPSO with binary coding. Two controller design methods of robots trajectory tracking are analyzed, parameters of them are optimized with weighted operator and the simulation results are compared. Simulation results show that QPSO algorithm and its improvemen...

Due to the difficulties in the measurement of biochemical variables in fermentation process, softsensing model based on radius basis function neural network had been established for estimating the variables. To generate a more efficient neural network estimator, we employed the previously proposed quantum-behaved particle swarm optimization (QPSO) algorithm for neural network training. The expe...

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by in...

In this paper, we formulate the dynamics and philosophy of Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, and suggest a parameter control method based on the whole population level. After that we introduce a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization...