This paper presents a synthetic analysis method for multi sourced g eo logical data from geographic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodol...This paper presents a synthetic analysis method for multi sourced g eo logical data from geographic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodology has been sta tistical analysis of cells delimitated based on thoughts of random sampling. Tha t might lead to insufficient utilization of local spatial information, for a cel l is treated as a point without internal structure. We now take “cell clusters ”, i. e. , spatial associations of cells, as basic units of statistics, thus th e spatial configuration information of geological variables is easier to be dete cted and utilized, and the accuracy and reliability of prediction are improved. We build a linear multi discriminating model for the clusters via genetic algor ithm. Both the right judgment rates and the in class vs. between class distan ce ratios are considered to form the evolutional adaptive values of the populati on. An application of the method in gold mineral resources prediction in east Xi njiang, China is presented.展开更多
An adaptive learning control scheme intended to the on-lineoptimization of sculptured. The scheme uses a back-propagation neuralnetwork to learn the relationships between process inputs and processstates. The cutting ...An adaptive learning control scheme intended to the on-lineoptimization of sculptured. The scheme uses a back-propagation neuralnetwork to learn the relationships between process inputs and processstates. The cutting parameters of the process model are optimizedthrough a genetic algorithms(GA). The capacity of the proposed schemefor determining optimum process inputs under a variety of processconditions and optimization strategies is evaluated on the basis ofmilling of a sculptured surface using a ball-end mill. Theexperimental results show that the neural network could model thecutting process efficiently, and the cutting conditions such asspindle speed could be regulated for achieving high efficiency andhigh quality. Therefore the proposed approach can be well applied tothe manufacturing of dies and molds.展开更多
A genetic neural net work m odel about design of Mg content in the alloy , based on tested databetw een Mg and tensile intensity or elongation in Zn 27 % Al alloy , has been established . Theresult has sho w n th...A genetic neural net work m odel about design of Mg content in the alloy , based on tested databetw een Mg and tensile intensity or elongation in Zn 27 % Al alloy , has been established . Theresult has sho w n that the genetic neural netw ork is a better an d m ore applied method for m a terials design than the regress analysis .展开更多
文摘This paper presents a synthetic analysis method for multi sourced g eo logical data from geographic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodology has been sta tistical analysis of cells delimitated based on thoughts of random sampling. Tha t might lead to insufficient utilization of local spatial information, for a cel l is treated as a point without internal structure. We now take “cell clusters ”, i. e. , spatial associations of cells, as basic units of statistics, thus th e spatial configuration information of geological variables is easier to be dete cted and utilized, and the accuracy and reliability of prediction are improved. We build a linear multi discriminating model for the clusters via genetic algor ithm. Both the right judgment rates and the in class vs. between class distan ce ratios are considered to form the evolutional adaptive values of the populati on. An application of the method in gold mineral resources prediction in east Xi njiang, China is presented.
文摘An adaptive learning control scheme intended to the on-lineoptimization of sculptured. The scheme uses a back-propagation neuralnetwork to learn the relationships between process inputs and processstates. The cutting parameters of the process model are optimizedthrough a genetic algorithms(GA). The capacity of the proposed schemefor determining optimum process inputs under a variety of processconditions and optimization strategies is evaluated on the basis ofmilling of a sculptured surface using a ball-end mill. Theexperimental results show that the neural network could model thecutting process efficiently, and the cutting conditions such asspindle speed could be regulated for achieving high efficiency andhigh quality. Therefore the proposed approach can be well applied tothe manufacturing of dies and molds.
文摘A genetic neural net work m odel about design of Mg content in the alloy , based on tested databetw een Mg and tensile intensity or elongation in Zn 27 % Al alloy , has been established . Theresult has sho w n that the genetic neural netw ork is a better an d m ore applied method for m a terials design than the regress analysis .
