The FCM algorithm was sensitive to noise data due to the normalized constraint of fuzzy membership.A novel clustering algorithm is proposed and named as relaxed fuzzy C-means clustering（RFCM） in this paper,the objec...
详细信息
The FCM algorithm was sensitive to noise data due to the normalized constraint of fuzzy membership.A novel clustering algorithm is proposed and named as relaxed fuzzy C-means clustering（RFCM） in this paper,the objective function of PCM is utilized as the objective function of RFCM,and RFCM loosens the normalized constraint and only requests the whole summation of n samples' fuzzy memberships equal to n and 0≤u≤1,particleswarm optimization algorithms（PSO） are optimally used to select the fuzzy memberships of RFCM,and the value scope of fuzzy index m is extended to m>0,the iterative formula of clustering centers are derived by gradient method for *** anti-noise performance of RFCM is analyzed theoretically,and the rationality of new value scope of m>0 is explained for RFCM,and the convergence of RFCM is discussed *** effectiveness and anti-noise performance of RFCM are proved through simulation experiments on two-dimensional Gaussian data-set for anti-noise and clustering accuracy tests.
The combination of cloud computing and advancements in GPU have made many real time services possible, including Cloud Gaming (CG). Doing all the process-intensive tasks in the cloud frees players from upgrading their...
详细信息
ISBN:
(纸本)9781538660980
The combination of cloud computing and advancements in GPU have made many real time services possible, including Cloud Gaming (CG). Doing all the process-intensive tasks in the cloud frees players from upgrading their heterogeneous devices and installing new software, and lets them play wherever and whenever. However, higher quality is always demanded by players. For instance, as frame rate has major impact on the player's gaming performance, demand of higher frame rate is increasing. On the other hand, service providers aim to offer cost effective services. Management of the graphic-intensive CG service demand and maximizing the service providers' benefits is an issue that must be addressed properly. As remote GPU plays the main role of rendering and is the most expensive infrastructure rented by the service provider, its appropriate management is vital to address the above issue. To do so, we formulate the problem in an efficient manner and propose two methods to maximize both GPU utilization and the users' quality of experience (QoE) at the same time, subject to the constraints of the servers. Our methods are based on two metaheuristic algorithms to solve an NP-Hard optimization problem for GPU-based server selection. Our simulation results shows that by increasing the number of players, both algorithms have increasing performance in terms of GPU utilization, reduced capacity wastage, and QoE.
This article mainly studied the performance of different neural networks in the processing network security situation prediction (NSSP). Radial basis function (RBF) and back propagation neural network (BPNN) models we...
详细信息
This article mainly studied the performance of different neural networks in the processing network security situation prediction (NSSP). Radial basis function (RBF) and back propagation neural network (BPNN) models were optimized by particleswarm optimization (PSO) algorithm and seeker optimization algorithm (SOA), respectively. Then the PSO-RBF model and SOA-BPNN model were obtained, and comparative experiments were carried out on CNCERT/CC data set. The results suggested that the improved models were more accurate in predicting the situation value compared with RBF and BPNNmodels;the PSO-RBF mode had three prediction errors, with 0.05 mean square error (MSE) and 0.05 mean absolute error (MAE), and the SOA-BPNNmodel had six prediction errors, with 0.2 MSE and 0.13 MAE, which showed that the PSO-RBF model had better performance. The experimental results show that the PSO-RBF model has an excellent performance in processing NSSP and can be promoted and applied in practice.
In this paper, we propose a forecasting model of electric power equipment statement assembled by core vector machines and particle swarm algorithm to improve the accuracy of electric equipment maintenance. The electri...
详细信息
In this paper, we propose a forecasting model of electric power equipment statement assembled by core vector machines and particle swarm algorithm to improve the accuracy of electric equipment maintenance. The electric power equipment condition forecasting model improves parameter selection problems of nuclear vector regression by particle swarm algorithm, optimizes parameters of kernel function and reduces the artificial factors in the forecasting process;accordingly reduces the blindness in the process of training and improves the accuracy of the prediction, while core vector regression have the advantages of high precision, suitable for power equipment maintenance process.
Fault prediction is of great importance to ensuring weapon equipments' safety and reliability. Usually the data for fault detection and prediction of weapon equipments have features like small samples and multi-pa...
详细信息
ISBN:
(纸本)9781450376617
Fault prediction is of great importance to ensuring weapon equipments' safety and reliability. Usually the data for fault detection and prediction of weapon equipments have features like small samples and multi-parameter. Currently the main fault prediction methods have achieved some success in practical applications, but all fall short at some aspects. Based on grey prediction theory and with analysis of disadvantages of GM(1, 1) model, an adaptive prediction model with several characteristic parameters for small samples is put forward. This model modifies initial value and background value, and takes interrelations of the parameters and characteristics of prediction series into account. The model is then used for prediction and analysis with the multi-parameter data of certain aero-engine. The results show that the model has good prediction precision, which in turn validates its availability.
Reducing thermal unit operating costs and emissions is the goal of the multi-objective issue known as multi-area economic/emission dispatch (MAEED) in smart grids. Using renewable energy (RE) have significantly lowere...
详细信息
Reducing thermal unit operating costs and emissions is the goal of the multi-objective issue known as multi-area economic/emission dispatch (MAEED) in smart grids. Using renewable energy (RE) have significantly lowered greenhouse gas emissions and ensured the sustainability of the environment. With regard to constraints such as prohibited operating zones (POZs), valve point effect (VPE), transmission losses in the network, ramp restrictions, tie-line capacity, this study aims to minimize operating costs and emission objectives by solving the multi-area dynamic economic/emission dispatch (MADEED) problem in the presence of RE units and energy storage (ES) systems. The conventional economic dispatch (ED) optimization approach has the following shortcomings: It is only designed to solve the single-objective optimization problem with a cost objective, in addition, it also does not have high calculation accuracy and speed. Therefore, to address this multi-objective MADEED problem with non-linear constraints, this paper introduces hybrid particleswarm optimization (PSO)-whale optimization algorithms (WOA). The reason for combining two algorithms is to use the advantages of both algorithms in solving the desired optimization problem. The introduced method is tested in two separate scenarios on a test network of 10 generators. Using the suggested hybrid methodology in this study, the MADED and MADEED problems are resolved and contrasted with other evolutionary techniques, such as original WOA, and PSO methods. Examining the results of the proposed method shows the efficiency and better performance of the proposed method compared to other methods. Finally, the results obtained by simulations indicate that integrating the necessary system restrictions gives the system legitimacy and produces dependable output. With regard to the results obtained from the introduced approach, the value of the overall cost function has clearly decreased by about 3 % compared to other methods.
To optimize the structure of an air route network, accurate forecasting of future new routes is vital given the rapid growth in demand for air transportation. Based on the theory and method of link prediction, conside...
详细信息
To optimize the structure of an air route network, accurate forecasting of future new routes is vital given the rapid growth in demand for air transportation. Based on the theory and method of link prediction, considering the joint influence of network endogenous factors and external attributes, we construct a system of network endogenous factors and external attribute indices and explore the prediction effect of each index. We further construct a prediction index system, explore the prediction effect of coupled indices, design a particle swarm algorithm to determine the weights of each index, and propose a coupled link prediction model based on particleswarm optimization (PSO-CLP). A comparison of the prediction accuracy of this model with the dual-indicator coupled link prediction model and the traditional link prediction model is also conducted to test the stability and reasonableness of the PSO-CLP model. In this study, we use the 2015-2020 Chinese air route network as an example. The instance test shows that the PSO-CLP models significantly outperform the traditional link prediction models and dual-indicator coupled link prediction models in terms of prediction accuracy, stability and computational simplicity, among which the PSO-CLP model, which considers both endogenous factors and external attributes, such as the RWR + Sor + Pop and RWR + RA + GDP indices, has the best forecasting effect. The PSO-CLP model is an effective tool for route prediction, providing a new perspective on route link prediction and air route network optimization.
particleswarm optimization (PSO) algorithm is such a simple, easily understood and realizable concept that it has been developing rapidly and has been applied widely since it was introduced. This paper proposes a mod...
详细信息
particleswarm optimization (PSO) algorithm is such a simple, easily understood and realizable concept that it has been developing rapidly and has been applied widely since it was introduced. This paper proposes a modified particleswarm optimization(MPSO) algorithm with novel initializing particleswarms to improve the performance of the standard PSO. It is tested with a benchmark function compared with the standard PSO. Experimental results indicate that MPSO performs much better than the standard PSO both in terms of the speed of solutions and robustness.
By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant ...
详细信息
By studying principles and methods related to early-warning model of plant diseases and using PSO method, parameter optimization was conducted to backward propagation neural network, and a pre-warning model for plant diseases based on particleswarm and neural network algorithm was established. The test results showed that the construction of early-warning model is effective and feasible, which will provide a via- ble model structure to establish the effective early-warning platform.
暂无评论