排序方式: 共有29条查询结果,搜索用时 46 毫秒
1.
2.
基于遗传神经网络的地震预测研究 总被引:3,自引:0,他引:3
分析遗传算法(GA)及BP神经网络的结构特性,提出利用具有全局搜索能力的遗传算法来弥补BP网络的不足,克服BP(Error Back Propagation)算法收敛速度慢,易陷入局部极小点的缺点,优化神经网络的连接权值和阈值.针对地震预测中,震级预测的困难性等问题,将具有全局搜索能力的遗传算法和具有深度搜索能力的BP算法相结合实现地震震级预测建模.通过实验比较得到了较好的预测结果,该模型是可行、有效的. 相似文献
3.
浅水湖泊直立沿岸带一般具有水深岸陡、水体污染严重、底泥不利于水生植物生长等特点,生态恢复困难.为了修复受损的浅水湖泊直立沿岸带,结合在滇池福保湾的生态恢复实践和国内外的相关研究,提出采用清洁底泥吹填技术减少水深、改善底质性状、创造多样性地形,为水生植被恢复创造有利的生境条件.清洁底泥吹填技术的工艺流程为,首先对工程区周边进行勘察,确定泥源;接着选用合适的疏挖设备按工程区基底修复设计的要求进行吹填施工;最后在基底修复满足设计要求的前提下实施水生植被种植等工程.该技术在滇池福保湾的应用显示,吹填工程实施2年后,工程区内水生植物得到初步恢复,恢复效果明显好于未采用该技术的恢复区. 相似文献
4.
5.
6.
7.
An orthogonal multi-objective evolutionary algorithm for multi-objective optimization problems with constraints 总被引:1,自引:0,他引:1
In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the selection process. Then, the orthogonal design and the statistical optimal method are generalized to MOPs, and a new type of multi-objective evolutionary algorithm (MOEA) is constructed. In this framework, an original niche evolves first, and splits into a group of sub-niches. Then every sub-niche repeats the above process. Due to the uniformity of the search, the optimality of the statistics, and the exponential increase of the splitting frequency of the niches, OMOEA uses a deterministic search without blindness or stochasticity. It can soon yield a large set of solutions which converges to the Pareto-optimal set with high precision and uniform distribution. We take six test problems designed by Deb, Zitzler et al., and an engineering problem (W) with constraints provided by Ray et al. to test the new technique. The numerical experiments show that our algorithm is superior to other MOGAS and MOEAs, such as FFGA, NSGAII, SPEA2, and so on, in terms of the precision, quantity and distribution of solutions. Notably, for the engineering problem W, it finds the Pareto-optimal set, which was previously unknown. 相似文献
8.
9.
10.