In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural netw...
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In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy.
The recently-developed magnetorheological elastomer (MRE) base isolator can provide an instant change in the shear modulus and damping property under applied magnetic field, which makes it as an ideal device for the s...
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The recently-developed magnetorheological elastomer (MRE) base isolator can provide an instant change in the shear modulus and damping property under applied magnetic field, which makes it as an ideal device for the semi-active control in buildings and bridges. Previous studies show that this new device is featured with its nonlinear and hysteretic responses, and it is necessary to sufficiently understand its behaviour when adopting this device in control system. Although there are several models presented to predict the hysteresis of MRE base isolator, they are always suffered from some application limitations, e.g. high computation demand or complex model. To better interpret this complicated feature of the device, this work presents an improved LuGre friction model, which has been successfully used in modelling other magnetorheological device i.e. MR damper. In addition, an improved fruit fly optimization algorithm (IFFOA) is also proposed to identify the model parameters. In the improved algorithm, a transfer factor based on a self-adaptive step is added together with a three-dimensional searching space. This improvement can enhance the convergence rate of the algorithm and avoid the local optimum. Furthermore, to reduce the complexity of the model, the local and global parameter sensitivity analyses are conducted for model simplification. Eventually, the experimental measurements of device displacement, velocity and shear force are used to evaluate the performance of the proposed model and IFFOA.
The microblog as a platform used to serve the communication of the internet,has been developing *** blog has become an important way to get information and to transmit information [1].The blog message can be transmitt...
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The microblog as a platform used to serve the communication of the internet,has been developing *** blog has become an important way to get information and to transmit information [1].The blog message can be transmitted,and we as consumer can transmit them by a few simple and convenient *** to the suddenly happened situation,the transmission can cause a very direct influence to the transmission and spread of the situation *** we set an example of the topic,the share of the photography course,and we also refer rbf neural network to the research of the transmission of the relevant situations,and we will continue the optimization of width status in the kernel function combining fruitflyoptimization ***,this essay analyzes different factors effecting blog's rewet counts in many different *** this base,we classify the blogs according to the rewet counts,and we predict the blogs' rewet counts by rbf basing on the foa,which has been *** different numbers' samples,the experimental result has certain reference value by contrasting and analyzing the predicted result of foa-rbf model.
Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on ...
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Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.
Many practical applications are used in set covering problems (SCP), in this research, we used to solve SCP: the binary fruit fly optimization algorithms. This algorithm is divided in four phases: initiation, smell ba...
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ISBN:
(纸本)9781479983308
Many practical applications are used in set covering problems (SCP), in this research, we used to solve SCP: the binary fruit fly optimization algorithms. This algorithm is divided in four phases: initiation, smell based search local vision based search and global vision based search. The metaheuristic is based by the knowledge from the foraging behavior of fruit-flies in finding food. The algorithm used a probability vector to improve the exploration. The tests were performed with eight different transfer functions and an elitist selection method. The test results show the effectiveness of the algorithm proposed.
A method to establish performance degradation model for barrel based on general regression neural network with fruit fly optimization algorithm(FOAGRNN) was proposed. It took the muzzle velocity reduction as performan...
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A method to establish performance degradation model for barrel based on general regression neural network with fruit fly optimization algorithm(FOAGRNN) was proposed. It took the muzzle velocity reduction as performance degradation feature with the increase in the number of shooting ammunition quantity under various working conditions, based on the performance degradation experimental data of barrel. The forecasting results were basically consistent with experimental results, which proved the feasibility of the method.
Parameter selection is an important issue for support vector *** solve that problem,an improved fruit fly optimization algorithm is proposed in this paper,and used for parameter selection of support vector *** initial...
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Parameter selection is an important issue for support vector *** solve that problem,an improved fruit fly optimization algorithm is proposed in this paper,and used for parameter selection of support vector *** initial position and the iteration step size are improved in the new algorithm,and it has stronger search *** and generality of the method are proved by experiment on standard data sets.
Using Probabilistic Neural Network (PNN) to recognize speaker is one of the research of branch of speaker recognition. PNN's ability of recognition is so dependent on the value of its smoothing factor that its abi...
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Using Probabilistic Neural Network (PNN) to recognize speaker is one of the research of branch of speaker recognition. PNN's ability of recognition is so dependent on the value of its smoothing factor that its ability of recognition is not that good. To solve this problem, we proposed a novel hybrid algorithm (DFOA-SOM-PNN) to improve PNN's ability of recognition. Firstly, it uses SOM to cluster MFCC speech characteristics parameters which can reduce storage of data and calculation, and good reflect feature of MFCC. Secondly, it uses an improved algorithm of fruit fly optimization algorithm (FOA): Double group FOA (DFOA), which optimizes the smooth factor of PNN. The experimental results show that DFOA have better global convergence and fast convergence speed than FOA, and the proposed hybrid algorithm has better performance in speaker recognition.
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