Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpred...Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.展开更多
In aquaculture,the accurate prediction of feed intake for group fish is considered to be crucial to any feeding system.Previous studies mainly used mathematical statistics to establish the mapping relationship between...In aquaculture,the accurate prediction of feed intake for group fish is considered to be crucial to any feeding system.Previous studies mainly used mathematical statistics to establish the mapping relationship between feed intake and influencing factors.The result was easily influenced by subjective experience.To solve the above issues,this paper proposed a feed intake prediction model for group fish using the back-propagation neural network(BPNN)and mind evolutionary algorithm(MEA).Firstly,four factors,including water temperature,dissolved oxygen,the average fish weight and the number of fish were selected as the input of the BPNN model.Secondly,the initial weight and threshold of the BPNN were optimized by the MEA to improve the matching precision.Finally,the prediction model was achieved after training.Experimental results showed that the correlation coefficient between the predicted and measured values reached 0.96.And the root mean squared error,mean square error,mean absolute error,mean absolute percent error of the model was 6.89,47.53,6.17 and 0.04,respectively.In addition,the proposed method also had the better nonlinear fitting ability than BPNN and GA-BP.By using an intelligent optimization algorithm,the mapping relationship between fish intake and environmental factors was automatically established,thus avoiding the subjectivity of traditional methods.Therefore,it can lay a theoretical foundation for the development of intelligent feeding equipment and meet the needs of the smart fishery.展开更多
文摘Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.
基金The work was supported by the Beijing Excellent Talents Development Project(2017000057592G125)National Key Research and Development Program of China(2017YFD0701700).
文摘In aquaculture,the accurate prediction of feed intake for group fish is considered to be crucial to any feeding system.Previous studies mainly used mathematical statistics to establish the mapping relationship between feed intake and influencing factors.The result was easily influenced by subjective experience.To solve the above issues,this paper proposed a feed intake prediction model for group fish using the back-propagation neural network(BPNN)and mind evolutionary algorithm(MEA).Firstly,four factors,including water temperature,dissolved oxygen,the average fish weight and the number of fish were selected as the input of the BPNN model.Secondly,the initial weight and threshold of the BPNN were optimized by the MEA to improve the matching precision.Finally,the prediction model was achieved after training.Experimental results showed that the correlation coefficient between the predicted and measured values reached 0.96.And the root mean squared error,mean square error,mean absolute error,mean absolute percent error of the model was 6.89,47.53,6.17 and 0.04,respectively.In addition,the proposed method also had the better nonlinear fitting ability than BPNN and GA-BP.By using an intelligent optimization algorithm,the mapping relationship between fish intake and environmental factors was automatically established,thus avoiding the subjectivity of traditional methods.Therefore,it can lay a theoretical foundation for the development of intelligent feeding equipment and meet the needs of the smart fishery.
