The existing sports training methods are mainly aimed at the sports field environment. The traditional sports training is only based on the coaches' visual inspection and combined with their own experience to put ...
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The existing sports training methods are mainly aimed at the sports field environment. The traditional sports training is only based on the coaches' visual inspection and combined with their own experience to put forward suggestions, which is relatively inefficient, thus limiting the rise of athletes' sports training level to a certain extent. Based on this background, combining traditional physical education teaching methods with video image processing technology, especially using particle swarm optimization algorithm, can promote the application of human motion recognition technology in physical training. This paper mainly investigates the optimization process of particle swarm optimization algorithm and discusses the development of particle swarm optimization algorithm;Secondly, through video decoding, image noise removal, video enhancement and other methods, complete video image processing and establish the structure of the manikin to achieve the collection of key points of the target, and then collect relevant data with experimental methods The results show that the motion recognition system proposed in this paper can effectively detect the changes of athletes' sampling point path, and can be compared with standard movements, which has a good auxiliary role. With the application of video image processing technology in sports training becoming more and more common, athletes can analyze their training videos in a more intuitive way and find out shortcomings, so as to improve the training effect. This paper studies particle swarm optimization algorithm and applies it to the field of video image processing, which promotes the development of sports action recognition technology based on video processing.
P300 is an important control system signal in the brain, so there is an urgent need and practical significance to work on the efficient classification of P300 event-related potentials. In this article, we design a con...
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ISBN:
(纸本)9798400707964
P300 is an important control system signal in the brain, so there is an urgent need and practical significance to work on the efficient classification of P300 event-related potentials. In this article, we design a convolutional neural network CNNnet based on chaotic adaptive particleswarmoptimization (CAPSO) algorithm for efficient and accurate detection and classification of P300 EEG signals. The chaotic adaptive particle swarm optimization algorithm uses Logistic chaotic mapping to initialize the initial position of particles, and adopts a dynamic adaptive weighting strategy. Compared with traditional particle swarm optimization algorithms, it can effectively improve the optimization speed and convergence speed of particles. The experimental results show that compared with other P300 detection neural networks and traditional particle swarm optimization algorithms, this algorithm has faster convergence speed and higher convergence accuracy, and can effectively avoid the problem of particleswarm falling into local optima.
As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significa...
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As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles-Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics.
In the field of optimizationalgorithms, hybrid algorithms are increasingly valued by researchers for their effectiveness in improving algorithmic *** recent years, a new type of natural meta-heuristic algorithm calle...
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ISBN:
(纸本)9781665426558
In the field of optimizationalgorithms, hybrid algorithms are increasingly valued by researchers for their effectiveness in improving algorithmic *** recent years, a new type of natural meta-heuristic algorithm called whale optimizationalgorithm has been proposed. The algorithm refers to whales in nature and imitates their three different feeding methods to solve realistic optimization problems. The particleswarmalgorithm, on the other hand, is an algorithm proposed by imitating the way a flock of birds transmits information. As population intelligence algorithms, the accuracy of these two algorithms are not high enough in the convergence process. At the same time, they tend to fall into the local optimum. In this paper, a hybrid algorithm based on whale optimizationalgorithm and particleswarmalgorithm is proposed to update the population by a kind of selection iteration. The experimental results confirm that the algorithm has excellent superiority in convergence accuracy and convergence speed.
In view of the manipulator system is a highly coupled, nonlinear dynamic characteristics and the system structure and parameters, such as there are many unpredictable factors in the practical work of multiple input mu...
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ISBN:
(纸本)9781450384162
In view of the manipulator system is a highly coupled, nonlinear dynamic characteristics and the system structure and parameters, such as there are many unpredictable factors in the practical work of multiple input multiple output system, designed a fuzzy neural network controller, and combined with particle swarm optimization algorithm for fuzzy neural network controller parameter setting. Through MATLAB simulation, it is proved that the scheme has strong robustness and stability for the control system, and effectively solves the trajectory tracking problem of manipulator.
Analog design can be considered as a multidimensional optimization problem since it involves trade-offs between several circuit parameters. Various optimization techniques have been proposed to reduce the cycle time o...
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ISBN:
(纸本)9781728192017
Analog design can be considered as a multidimensional optimization problem since it involves trade-offs between several circuit parameters. Various optimization techniques have been proposed to reduce the cycle time of analog design. We propose a hybrid particle swarm optimization algorithm with linearly decreasing inertia weight for the optimization of analog circuit design. The proposed method is validated in a differential amplifier circuit with a current mirror load. Promising simulation results demonstrate that the proposed method can significantly reduce the design time required for analog circuits.
To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the informatio...
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To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the information of 'excellent' infeasible solutions. The algorithm uses this information to escape from the local best solution and quickly converge to the global best solution. Additionally, to further improve the global search ability of the algorithm, the DE strategy is used to optimize the personal best position of the particle, which speeds up the convergence speed of the algorithm. The performance of our method was tested on 24 benchmark problems from IEEE CEC2006 and three real-world constraint optimization problems from CEC2020. The simulation results show that the CPSO algorithm is effective.
Concerning the issue of low energy recovery efficiency of compound braking in Battery electric vehicles, an electromechanical composite braking control strategy based on road recognition and particleswarm optimizatio...
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Concerning the issue of low energy recovery efficiency of compound braking in Battery electric vehicles, an electromechanical composite braking control strategy based on road recognition and particle swarm optimization algorithm is proposed, which is different from the braking force control strategy of front and rear wheels with fixed ratio distribution before optimization. By analyzing the structure and working principle of the compound braking system of electric vehicle, a road surface identifier based on fuzzy algorithm is designed to track the peak adhesion coefficient of the road surface to obtain the maximum braking force of the brake. In addition, particleswarmoptimization (PSO) is used to modify the motor braking force, so as to maximize the efficiency of energy recovery, and CRUISE and MATLAB are used in simulation environment to carry out joint simulation analysis. The results show that compared with the control strategy before optimization, the proposed control strategy not only ensures the vehicle braking stability but also has shorter braking distance, shorter braking time, larger motor braking torque, and slower decrease of battery State of Charge (SOC) value. Under the cycle condition of New European Driving Cycle (NEDC), Federal Test Procedure (FTP) and Extra Urban Driving Cycle (EUDC), the State of Charge of the battery increased by 1.98%, 2.1%, and 0.5%, respectively.
Generally, the optimization of injection and production parameters in oilfields are carried out by using reservoir engineering method or mathematical algorithm individually, which limits the optimization efficiency an...
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Generally, the optimization of injection and production parameters in oilfields are carried out by using reservoir engineering method or mathematical algorithm individually, which limits the optimization efficiency and accuracy. To deal with this problem, the paper tries to improve production optimization performance by introducing reservoir engineering method into conventional particleswarmoptimization (PSO) in three ways: the preprocessing result by reservoir engineering method is used respectively as population initialization, the search space constraint and the particle velocity guide item in PSO. Results show that all the three improved optimization methods can speed up the convergence rate of PSO algorithm while keeping similar convergence results at the same time. Furthermore, the use of the reservoir engineering preprocessing scheme as the search space constraint obtains the best convergence performance and reduces the iteration calculations by 24.14%, providing an effective way to reduce calculation cost for reservoir production optimization in commercial oilfields.
The optimization of injection-production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this pr...
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The optimization of injection-production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particleswarmalgorithm, the net present value of the improved scheme was increased by about 3.3%.
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