Cardiopulmonary Function Test of Athletes is the Key to Scientifically and Reasonably Formulate Training Plans. in Order to Solve the Problem of Large Errors in the Existing Cardiorespiratory Function Detection Method...
详细信息
Cardiopulmonary Function Test of Athletes is the Key to Scientifically and Reasonably Formulate Training Plans. in Order to Solve the Problem of Large Errors in the Existing Cardiorespiratory Function Detection Methods, a Multiple Linear Regression Cardiorespiratory Function Detection Method Based on particleswarmoptimization is Proposed. through Significant Difference Correlation Evaluation, the Metabolic Circulation Function in Sports is Analyzed to Realize Comprehensive Evaluation of Athletes' Absolute Strength, Speed Strength and Strength Endurance, and the Internal Relationship between Athletes' Aerobic Metabolism Ability and Anaerobic Metabolism Ability is Obtained. the Results Show That 3 Months Aerobic Exercise Can Obviously Improve the Body Shape and Physiological Function of Young Women. particleswarmoptimization is Used to Optimize and Improve the Speed and Accuracy of Cardiopulmonary Function Detection. the Method Can Effectively Improve the Cardiopulmonary Function of Athletes Before and after Aerobic Training, and Has High Modeling Accuracy.
Membrane fouling reduces wastewater treatment efficiency and cause financial and energy costs to some extent. The size of membrane flux reflects the degree of membrane pollution. Make timely cleaning membrane or repla...
详细信息
ISBN:
(数字)9781538682463
ISBN:
(纸本)9781538682463
Membrane fouling reduces wastewater treatment efficiency and cause financial and energy costs to some extent. The size of membrane flux reflects the degree of membrane pollution. Make timely cleaning membrane or replacement membrane decision to maintain considerable treatment effect on the basis of the membrane flux. particleswarmoptimization (PSO) algorithm can quickly find the global optimum. Genetic algorithm (GA) has the property of global convergence. The prediction model used in this paper is based on the PSO-GA hybrid algorithm. The combination of these not only improves the convergence speed of genetic algorithm but also reduces the probability of particle swarm optimization algorithm falling into local optimum. Elman neural network acts as the basic network. Compared with Elman neural network and BP neural network, the prediction accuracy of PSO-GA-Elman is improved.
particleswarmoptimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm i...
详细信息
particleswarmoptimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm is summarized in several fields, such as parameter selection and design, population topology, hybrid PSO algorithm etc. Finally, some vital applications and aspects that may be conducted in the future investigations are discussed.
For the problem of particleswarmoptimization parameters selection, a kind of intelligent method to optimum parameters selection using another particleswarmoptimizationalgorithm is proposed. Firstly it analyze...
详细信息
For the problem of particleswarmoptimization parameters selection, a kind of intelligent method to optimum parameters selection using another particleswarmoptimizationalgorithm is proposed. Firstly it analyzes the effect of each parameter on algorithm performance in detail. Then it takes parameter selection of PSO algorithm as a complex optimization problem, sets appropriate fitness function to describe optimization performance, and uses PSO-PARA algorithm to optimize the parameters selection method of PSO-OPT algorithm. Tests to the benchmark function show that these parameters are better than the experience parameters test results in the optimal fitness, the mean value of optimal fitness, convergence rate.
Effective diagnosis of rotating machinery is difficult in view of the complex structure, weak early fault signals, non-stationary and non-linear vibration signals, and low signal-to-noise ratio. In this paper, a fault...
详细信息
Effective diagnosis of rotating machinery is difficult in view of the complex structure, weak early fault signals, non-stationary and non-linear vibration signals, and low signal-to-noise ratio. In this paper, a fault diagnosis method is proposed based on particleswarmoptimization (PSO) and variational modal decomposition (VMD). Firstly, wavelet packet threshold is denoised on the signal, VMD is decomposed on the reconstructed signal, and PSO is optimized on the inherent mode function (IMF) obtained from decomposition so as to determine the best IMF function. Then Hilbert transform and envelope spectrum analysis are carried out on the IMF function, and the envelope spectrum analysis result is compared with theoretical calculation frequency to finally determine the fault type. The results indicate that this method can effectively reduce noise components in signals, extract weak fault information and realize fault diagnosis.
In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the m...
详细信息
In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the most important parameter for option pricing. Furthermore, the two non-parametric models based on Artificial Neural Networks and Neuro-Fuzzy Networks tuned by particle swarm optimization algorithm are proposed to price call options for the S&P 500 index. By comparing the results obtained using these models, we conclude that both Neural Network and Neuro-Fuzzy Network models outperform the Black-Scholes model.
In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear rela...
详细信息
In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear relationship between SOC and its influencing factors, RBF neural network has obvious advantages in solving nonlinear problems, so in this paper, an SOC estimation method of power battery based on RBF neural network is proposed. In order to improve the accuracy of SOC estimation, we use particleswarmoptimization (PSO) to optimize the RBF neural network model and identify the value of RBF network center vector and the weights through global optimal searching ability of PSO algorithm. The results simulation show that the SOC model based on PSO-RBF neural network has good estimation accuracy.
By introducing the adaptive inertia weight, the time factor and the structure rebuilding of particleswarmoptimization (PSO), the improvements of PSO are completed. In order to improve the accuracy and convergence sp...
详细信息
ISBN:
(纸本)9781467371896
By introducing the adaptive inertia weight, the time factor and the structure rebuilding of particleswarmoptimization (PSO), the improvements of PSO are completed. In order to improve the accuracy and convergence speed, a PSO strategy is proposed, which consists of the dynamic population structure, opposition-based learning, crossover operator and variable step integral. Combining the improvement of PSO and the optimization strategy, the modified particleswarmoptimization ( MPSO) algorithm is formed. The MPSO is applied to optimize the ascent trajectory of hypersonic vehicle. The precision and efficiency of this trajectory optimization method are demonstrated by comparing the results of PSO and MPSO. The simulation results show that the performance of MPSO is significantly superior to PSO either convergence speed or convergent accuracy.
This study intends to present a dynamic clustering (DC) approach based on particleswarmoptimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through d...
详细信息
This study intends to present a dynamic clustering (DC) approach based on particleswarmoptimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.
This paper considers a single server retrial queue in which a state-dependent service policy is adopted to control the service rate. Customers arrive in the system according to a Poisson process and the service times ...
详细信息
This paper considers a single server retrial queue in which a state-dependent service policy is adopted to control the service rate. Customers arrive in the system according to a Poisson process and the service times and inter-retrial times are all exponentially distributed. If the number of customers in orbit is equal to or less than a certain threshold, the service rate is set in a low value and it also can be switched to a high value once this number exceeds the threshold. The stationary distribution and two performance measures are obtained through the partial generating functions. It is shown that this state-dependent service policy degenerates into a classic retrial queueing system without control policy under some conditions. In order to achieve the social optimal strategies, a new reward-cost function is established and the global numerical solutions, obtained by Canonical particle swarm optimization algorithm, demonstrate that the managers can get more benefits if applying this state-dependent service policy compared with the classic model.
暂无评论