With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it...In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it adopts rolling optimum method, system展开更多
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digita...The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.展开更多
This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method ...This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.展开更多
There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill l...There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters.展开更多
生态学研究领域中对智能算法的使用呈现越来越丰富的趋势,其解决了许多重要问题。智能算法的应用已逐渐成为生态学研究的重要话题。研究以中国知网(CNKI核心)和Web of Science核心数据库中42439篇智能算法在生态学领域应用的相关学术论...生态学研究领域中对智能算法的使用呈现越来越丰富的趋势,其解决了许多重要问题。智能算法的应用已逐渐成为生态学研究的重要话题。研究以中国知网(CNKI核心)和Web of Science核心数据库中42439篇智能算法在生态学领域应用的相关学术论文为依据,借助文献计量学软件CiteSpace.6.3R1,介绍2013—2023年间国内外研究热点的发展现状和情况;根据每种智能算法在生态学优化、预测和评估研究中的作用,分类论述其实际研究过程和应用特征;分析智能算法应用的优势和当前存在的局限性;回顾智能算法对生态学研究的意义,并提出了对未来发展前景的展望。展开更多
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
文摘In view of DC speed control system, this paper presents a predictive control algorithm to replace traditional PID control. System predictive model requires little information of the controlled object, and because it adopts rolling optimum method, system
基金Project(E2015203354)supported by Natural Science Foundation of Steel United Research Fund of Hebei Province,ChinaProject(ZD2016100)supported by the Science and the Technology Research Key Project of High School of Hebei Province,China+1 种基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,ChinaProject(16LGY015)supported by the Basic Research Special Breeding of Yanshan University,China
文摘The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
基金Projects(61573052,61273132)supported by the National Natural Science Foundation of China
文摘This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.
文摘There are few methods of semi-autogenous(SAG)mill power prediction in the full-scale without using long experiments.In this work,the effects of different operating parameters such as feed moisture,mass flowrate,mill load cell mass,SAG mill solid percentage,inlet and outlet water to the SAG mill and work index are studied.A total number of185full-scale SAG mill works are utilized to develop the artificial neural network(ANN)and the hybrid of ANN and genetic algorithm(GANN)models with relations of input and output data in the full-scale.The results show that the GANN model is more efficient than the ANN model in predicting SAG mill power.The sensitivity analysis was also performed to determine the most effective input parameters on SAG mill power.The sensitivity analysis of the GANN model shows that the work index,inlet water to the SAG mill,mill load cell weight,SAG mill solid percentage,mass flowrate and feed moisture have a direct relationship with mill power,while outlet water to the SAG mill has an inverse relationship with mill power.The results show that the GANN model could be useful to evaluate a good output to changes in input operation parameters.
文摘生态学研究领域中对智能算法的使用呈现越来越丰富的趋势,其解决了许多重要问题。智能算法的应用已逐渐成为生态学研究的重要话题。研究以中国知网(CNKI核心)和Web of Science核心数据库中42439篇智能算法在生态学领域应用的相关学术论文为依据,借助文献计量学软件CiteSpace.6.3R1,介绍2013—2023年间国内外研究热点的发展现状和情况;根据每种智能算法在生态学优化、预测和评估研究中的作用,分类论述其实际研究过程和应用特征;分析智能算法应用的优势和当前存在的局限性;回顾智能算法对生态学研究的意义,并提出了对未来发展前景的展望。
