| 1. | Study of collision detection algorithm optimization based on hycd
基于并行的快速碰撞检测算法 |
| 2. | Research of svm - based image interpolation algorithm optimization
基于向量机的图像插值算法研究 |
| 3. | The application of genetic algorithm optimization toolbox in matlab
环境下遗传算法优化工具箱的应用 |
| 4. | Algorithm optimization and code optimization of ct image reconstruction
高炉计算机断层成象技术成象算法的研究 |
| 5. | Genetic algorithm optimization of crank - rocker mechanism applying fuzzy design
应用模糊设计的曲柄摇杆机构遗传算法优化 |
| 6. | Application of lmmunogenetic algorithm optimization in intersection signal timing
免疫遗传算法在交叉口信号配时优化中的应用 |
| 7. | Genetic algorithm optimization based nonlinear ridge regression modeling method and its application in soft measurement
优化的非线性岭回归方法及其在软测量中的应用 |
| 8. | This dissertation mainly focuses on the jpeg decoding algorithm optimization and its realization in the embed system , shows the core of software realization
文中主要就多处理器嵌入式系统环境下jpeg解码算法的优化和实现作为重点,展示了软件实现的核心。 |
| 9. | _ _ _ _ uncertain factors of macroscale inversion analysis of displacements are summed up . associated inversion model containing non - deterministic factors is proposed , i . e . " deterministic inversion of differential equation + systematic optimization technique = non - deterministic inversion " . the systematic optimization technique includes direct operator optimization , direct numerical analysis optimization , measurement design optimization , measured data processing , in - ersion algorithm optimization , and inverse operator regularization , etc . when this associated inversion technique is used in displacements back analysis , uncertain factors can be processed quantitatively
归纳了宏观尺度位移反演分析的不确定性因素，提出了容纳不确定性因素的位移反演分析的联合反演模式，即“微分方程确定性反演+系统性优化技术=非确定性反演”的模式，并具体论述了联合反演模式的系统性优化技术，包括正演算子的优化、正演数值分析的优化、测量设计优化、观测数据处理、反演算法优化、反演算子处理等六个优化方法。 |
| 10. | This algorithm easily escapes from local optimal solution , have high searching efficiency , simple structure , convenient use . aiming at iteration , optimization and matlab optimization toolbox having low precision and difficulty to choose initial vector on acquiring nonlinear equations ’ solutions , equations ’ solution problem is translated into genetic algorithm optimization problem . nonlinear equations ’ usual genetic
针对迭代法、最优法、 matlab最优化工具箱求解非线性方程组中存在求解精度不高及初始矢量难选等问题，将方程组求解问题转化为遗传算法函数优化问题，建立了非线性方程组通用的遗传算法解法，并将其用于汽车滑行试验数据处理中。 |
