Application of bp artificial neural network and genetic algorithm to the parameters optimization of profile extrusion die 人工神经网络与遗传算法在型材挤压模具参数优化中的应用
From the point of the combination of both nn and ga , to research the intelligent identification technology for concrete structures is conducive to the practical application of engineering 本文从神经网络与遗传算法的结合出发,研究结构损伤智能识别技术,具有较强的工程背景和实际应用价值。
The data mining model combined with neural networks algorithm and genetic algorithm make the mf nodes arrangement and establishment location predictable and auto - adaptive under the future conditions 采用神经网络与遗传算法相结合的数据挖掘模型,能够使整个物流网络布局和设施选址对于未来的情况具有预测性和自适应性。
In this paper , a set of composites liquid - solid extrusion application software is complied by using of the visual c + + , matlab and access software . the visualization and interaction are realized through this software , what ' s more , the functions of fuzzy neural model setting up , parameter prediction , parameter optimization , figure exporting and database accessing are included . the composite liquid - solid extrusion process parameters can be predicted and optimized by use of the software 本文利用microsoftvc6 . 0 、 matlab以及microsoftaccess等软件,自行编制了一套基于模糊神经网络与遗传算法的集工艺建模、参数预测、参数优化、图形输出及数据库访问为一体的液-固挤压成形工艺应用软件,实现了该软件的可视化及良好的交互性。
The paper discuss the way to this question and want to explain the question by neural networks and ga with the help of projection arithmetic . the algorithm uses complexion model to detect karst object . first , the paper introduce the important of the research . then the paper understand the algorithm of patterm recognition and apply it to the images of remote sensing in jinping karst area 因此,本文先归纳和分析了当前遥感图象处理与模式识别的典型算法,然后利用目前流行的神经网络与遗传算法结合高斯-克吕格投影等平差分析算法进行遥感图象中的岩溶地物信息模式识别。