Particle size distributions of obtained samples from several sampling campaigns were determined and raw data were mass balanced before being used in simulation studies.After determination of breakage function,selectio...Particle size distributions of obtained samples from several sampling campaigns were determined and raw data were mass balanced before being used in simulation studies.After determination of breakage function,selection function,Bond work index,residence time distribution parameters,and Whiten's model parameters for air separators and diaphragms between the two compartments of tube ball mills,performance of the circuits was simulated for given throughputs and feed particle size distribution.Whiten's model parameters were determined by GA(genetic algorithm) toolbox of MATLAB software.Based on implemented models for modeling and simulation,optimization of circuits was carried out.It increased nearly 10.5% and 15.8% in fresh feed capacity input to each tube ball mill.In addition,circulating load ratios of circuits are modified to 118% and 127% from low level of 57% and 22%,respectively,and also cut points of air separators are adjusted at 30 and 40 μm from high range of 53 and 97 μm,respectively.All applications helped in well-operation and energy consumption reduction of equipments.展开更多
The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of...The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.展开更多
Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher acc...Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.展开更多
Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance o...Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.展开更多
基金financially supported by University of Tehran under contract number 450/51027041 with Iran Ministry of Industries and Mines
文摘Particle size distributions of obtained samples from several sampling campaigns were determined and raw data were mass balanced before being used in simulation studies.After determination of breakage function,selection function,Bond work index,residence time distribution parameters,and Whiten's model parameters for air separators and diaphragms between the two compartments of tube ball mills,performance of the circuits was simulated for given throughputs and feed particle size distribution.Whiten's model parameters were determined by GA(genetic algorithm) toolbox of MATLAB software.Based on implemented models for modeling and simulation,optimization of circuits was carried out.It increased nearly 10.5% and 15.8% in fresh feed capacity input to each tube ball mill.In addition,circulating load ratios of circuits are modified to 118% and 127% from low level of 57% and 22%,respectively,and also cut points of air separators are adjusted at 30 and 40 μm from high range of 53 and 97 μm,respectively.All applications helped in well-operation and energy consumption reduction of equipments.
基金Project(2007CB714006) supported by the National Basic Research Program of China Project(90815023) supported by the National Natural Science Foundation of China
文摘The hybrid genetic algorithm is utilized to facilitate model parameter estimation.The tri-dimensional compression tests of soil are performed to supply experimental data for identifying nonlinear constitutive model of soil.In order to save computing time during parameter inversion,a new procedure to compute the calculated strains is presented by multi-linear simplification approach instead of finite element method(FEM).The real-coded hybrid genetic algorithm is developed by combining normal genetic algorithm with gradient-based optimization algorithm.The numerical and experimental results for conditioned soil are compared.The forecast strains based on identified nonlinear constitutive model of soil agree well with observed ones.The effectiveness and accuracy of proposed parameter estimation approach are validated.
基金Project(61072087) supported by the National Natural Science Foundation of ChinaProject(20093048) supported by Shanxi ProvincialGraduate Innovation Fund of China
文摘Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.
基金Project(513300303)supported by the General Armament Department,China
文摘Gaussian process(GP)has fewer parameters,simple model and output of probabilistic sense,when compared with the methods such as support vector machines.Selection of the hyper-parameters is critical to the performance of Gaussian process model.However,the common-used algorithm has the disadvantages of difficult determination of iteration steps,over-dependence of optimization effect on initial values,and easily falling into local optimum.To solve this problem,a method combining the Gaussian process with memetic algorithm was proposed.Based on this method,memetic algorithm was used to search the optimal hyper parameters of Gaussian process regression(GPR)model in the training process and form MA-GPR algorithms,and then the model was used to predict and test the results.When used in the marine long-range precision strike system(LPSS)battle effectiveness evaluation,the proposed MA-GPR model significantly improved the prediction accuracy,compared with the conjugate gradient method and the genetic algorithm optimization process.
