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231.
本文把神经网络方法引进地震预报研究当中。使用地震频次,最大震级,平均震级,等价地震次数等多项地震活动性指标作为神经网络的输入,未来时段内的最大地震震级作为其输出,可以对某一固定地区的最大地震震级作出中近期预报。选用的神经网络模型为含两个中间层的前向模型,并采用BP算法。所得结果表明,用神经网络方法可以在一定精度范围内使震级预报的内检符合率达到100%,在本文的例子中,外推预报准确率达到60%以上。 相似文献
232.
介绍了BP网络的特点,阐述了它在位场资料解释中应用的方法、特点和可行性。通过模型计算证实了该方法在位场异常识别中的效果,正确率达97%。此外,通过修改网络单元的激发函数,增强网络的稳定性和内插功能,使BP网络在多层密度界面反演中得到满意的结果,模型和实例计算证明了该方法的有效性。 相似文献
233.
膨胀土判别与分类的人工神经网络方法 总被引:18,自引:0,他引:18
提出了运用神经网络理论对膨胀土进行判别与分类的方法。通过对安康膨胀土的实测数据分析,建立了研究膨胀土判别与分类的计算机智能专家系统。结果表明,用人工神经网络方法对膨胀土进行判别与分类的准确率很高,它无需在判别因子与预测目标之间建立基于某种理论的经验统计关系,该方法具有重要的实用价值。 相似文献
234.
The Heihe River drainage basin is one of the endangered ecological regions of China. The shortage of water resources is the bottleneck, which constrains the sustainable development of the region. Many scholars in China have done researches concerning this problem. Based on previous researches, this paper analyzed characteristics, tendencies, and causes of annual runoff variations in the Yingluo Gorge (1944–2005) and the Zhengyi Gorge (1954–2005), which are the boundaries of the upper reaches, the middle reaches, and the lower reaches of the Heihe River drainage basin, by wavelet analysis, wavelet neural network model, and GIS spatial analysis. The results show that: (1) annual runoff variations of the Yingluo Gorge have principal periods of 7 years and 25 years, and its increasing rate is 1.04 m3/s·10y; (2) annual runoff variations of the Zhengyi Gorge have principal periods of 6 years and 27 years, and its decreasing rate is 2.25 m3/s·10y; (3) prediction results show that: during 2006–2015, annual runoff variations of the Yingluo and Zhengyi gorges have ascending tendencies, and the increasing rates are respectively 2.04 m3/s·10y and 1.61 m3/s·10y; (4) the increase of annual runoff in the Yingluo Gorge has causal relationship with increased temperature and precipitation in the upper reaches, and the decrease of annual runoff in the Zhengyi Gorge in the past decades was mainly caused by the increased human consumption of water resources in the middle researches. The study results will provide scientific basis for making rational use and allocation schemes of water resources in the Heihe River drainage basin. 相似文献
235.
神经网络作为一种新的方法体系,具有分布并行处理、非线性映射、自适应学习和鲁棒容错等特性,在模式识别、控制优化和智能信息处理等方面有着广泛的应用。利用MatLab的神经网络工具箱,建立了江苏矿山地质环境质量的评估模型,评估结果经过实际验证,具有较高的可信度和实用性。 相似文献
236.
Anomaly analysis is used for various geophysics applications such as determination of geophysical structure's location and
border detections. Besides the classical geophysical techniques, artificial intelligence based image processing algorithms
have been found attractive for geophysical anomaly analysis. Recently, cellular neural networks (CNN) have been applied to
geophysical data and satisfactory results are reported. CNN provides fast and parallel computational capability for geophysical
image processing applications due to its filtering structure. The behavior of CNN is defined by two template matrices that
are adjusted by a properly supervised learning algorithm. After training stage for geophysical data, Bouguer anomaly maps
can be processed and analyzed sequentially. In this paper, CNN learning and processing capability have been improved, combining
Wavelet functions and backpropagation learning algorithms. The new architecture is denoted as Wavelet-Cellular Neural networks
(Wave-CNN) and it is employed to analyze Bouguer anomaly maps which are important to extract useful information in geophysics.
At first, Wave-CNN performance is tested on synthetic geophysical data, which are created by a computer environment. Then,
Bouguer anomaly maps of the Dumluca iron ore field have been analyzed and results are reported in comparison to real drilling
results. 相似文献
237.
WenJun Zhang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(6):729-735
This study aimed to evaluate effectiveness and performance of several supervised neural network models and make pattern recognition
on invertebrate habitat zones. Probabilistic, general regression, and linear neural networks, and discriminant analysis were
used to recognize both known and unknown invertebrate habitat zones. The results showed that neural network models were better
than traditional discriminant analysis in the recognition of known habitat zones. There was not distinctive variation in recognition
from different neural network models. Sensitivity analysis indicated that the learning rate of the neural network would influence
recognized results. An unknown invertebrate species from Lepidoptera was recognized to be soil-dweller (dryland) by both neural
network models and discriminant analysis. In sensitivity analysis it was additionally recognized to be the type of plant canopy
(terrestrial). Overall the species was estimated to be a soil-dweller (dryland) or live on plant canopy (terrestrial). It
was concluded that neural network models can perform better than conventional statistic models in pattern recognition, but
a comprehensive comparison among various models is necessary in order to achieve a high reliable recognition and prediction.
Furthermore, sensitivity analysis can lead to an in-depth grasp on the mechanism in the recognition and is thus needed. 相似文献
238.
采取平面网与高程网相结合的方法布设了施工控制网,并引用工程控制网优化设计的有关理论,结合实际对平面网的网形进行了机助优化。在网形确定的基础上,文中采用可靠因子为质量指标对网中的观测量进行优化,最终得到了满足精度和可靠性要求的最优布设方案。 相似文献
239.
240.