矿井煤层底板突水预测新方法研究

本文针对煤矿矿井煤层底板突水系统为一非线性系统的特性,提出采用对非线性问题具有良好适用性的人工神经网络系统（以下简称神经网络）,进行煤层底板突水预测。以作者们研制,使用神经网络的实践为基础,阐述系统、建模方法、适用条件和应用问题,并在焦作矿务局演马庄矿、焦作金科尔集团方庄煤矿对所建立的煤层底板突水预测神经网络进行生产性检验,取得良好的结果,说明该系统应用于煤层底板突水预测的可靠性。     

人工神经网络方法在桩基检测中的应用

提出了一种基于桩基动测波动数据的人工神经网络识别方法,所建立的多层神经网络通过对已知样本的学习,可实现对未学习样本（桩基检测数据）的识别,指出桩基中存在的缺陷。实际检验的结果表明该方法具有良好的应用前景     

神经网络实时诊断与优化模型建模法——人工智能在钻井工程中的应用之二

提出了一些新的实时诊断和钻压优化模型建模方法———神经网络法,它们可以克服传统方法需要建立数学模型的缺陷,满足钻进过程控制对实时性的要求。给出了利用反向传播神经网络（ＢＰ网络） 进行实时诊断和建立钻压优化模型的方法。实际应用和计算机仿真研究表明：采用这些新方法可以实时地实现钻进过程的事故诊断,建立的模型不但能够满足自动送钻实时优化钻压的要求,而且也可以用于离线的钻压参数优选     

人工神经网络在遥感中的应用与发展

介绍了国内外人工神经网络在遥感领域的应用现状,对神经网络的基本原理、技术优势、应用方法等进行了透视,并结合自己的研究实践,给出了具体的应用事例。     

用神经网络方法转换GPS高程

本文提出用神经网络方法转换ＧＰＳ高程为正高或正常高,给出一种改进了的ＢＰ神经网络拓扑结构和算法,并用ＧＰＳ的实际定位资料构成４３个样本集作了在计算分析,估算的精度达到厘米级、最后用网络方法与二次多项式曲面拟合大地水准面转换ＧＰＳ高程的方法作了比较,神经网络方法的精度优于二次多项式曲面拟合法,而且精度比较稳定,对已知样本点的数量要求较少。     

利用人工神经网络建立我国南海地区电离层模式

为了解决我国南海地区短波无线电通信信道的选择问题,本文建立了该区域一种新的电离层模式．模式中使用了人工神经网络技术,神经网络选用了５个预报变量,并用南海地区的６个电离层站的观测资料训练网络．结果表明：这个模式能够反映我国低纬电离层随太阳活动水平、经纬度和时间的变化,它适用于我国南海和东南海地区高频通信的频率预报．     

A multi-data training method for a deep neural network to improve the separation effect of simultaneous-source data

Within the field of seismic data acquisition with active sources, the technique of acquiring simultaneous data, also known as blended data, offers operational advantages. The preferred processing of blended data starts with a step of deblending, that is separation of the data acquired by the different sources, to produce data that mimic data from a conventional seismic acquisition and can be effectively processed by standard methods. Recently, deep learning methods based on the deep neural network have been applied to the deblending task with promising results, in particular using an iterative approach. We propose an enhancement to deblending with an iterative deep neural network, whereby we modify the training stage of the deep neural network in order to achieve better performance through the iterations. We refer to the method that only uses the blended data as the input data as the general training method. Our new multi-data training method allows the deep neural network to be trained by the data set with the input patches composed of blended data, noisy data with low amplitude crosstalk noise, and unblended data, which can improve the ability of the deep neural network to remove crosstalk noise and protect weak signal. Based on such an extended training data set, the multi-data training method embedded in the iterative separation framework can result in different outputs at different iterations and converge to the best result in a shorter iteration number. Transfer learning can further improve the generalization and separation efficacy of our proposed method to deblend the simultaneous-source data. Our proposed method is tested on two synthetic data and two field data to prove the effectiveness and superiority in the deblending of the simultaneous-source data compared with the general training method, generic noise attenuation network and low-rank matrix factorization methods.     

Stochastic ground motion models to NGA-West2 and NGA-Sub databases using Bayesian neural network

Ground motion models (GMMs) are traditionally developed from a frequentist approach. The Bayesian framework has received recent attention in developing nonergodic models, measuring uncertainty, or updating the model with additional data. However, no neural networks are developed to date in this framework to predict ground motion parameters or spectra. Hence, the present work develops a probabilistic Bayesian neural network (PBNN) to next-generation attenuation – West2 and Subduction databases using variational inference with mean-field assumption. Network inputs are magnitude, rupture distance, hypocentral depth, shear wave velocity, style of faulting, and region flags; outputs are peak ground values and response spectra. Both models have two hidden layers with seven neurons in each hidden layer. The models are verified for potential overfit, and their performance is validated through the parametric study by varying inputs. The output of a deterministic model is a point estimate. Considering probabilistic layers in hidden and output layers enables the model to capture within-model epistemic uncertainty and aleatory variability. Obtained aleatory standard deviations are consistent with other models. Mean epistemic uncertainty and aleatory variability are in the range 0.07–0.10 and 0.62–0.78 (ln units) for NGA-West2 and 0.09–0.16 and 0.67–0.95 for NGA-Sub models, respectively. The correlation coefficients between recorded and overall mean predictions ranged from 0.94 to 0.97 for NGA-the West2 model and from 0.91 to 0.95 for the NGA-Sub models. Network performance for out-of-training inputs showed increased epistemic deviations with no effect on aleatory deviations.     

降雨量及叠加预测方法研究

为了得到精确度较高的降雨量预测值及其叠加预测精度,利用小波神经网络和NARX动态神经网络对降雨趋势和降雨量进行预测,并分析降雨量叠加预测值的误差.研究表明,小波神经网络分析的月降雨量多个变化周期以及总的变化趋势较为准确;NARX动态神经网络预测模型测试误差为0.21％,回归效果图的相关系数R为0.99993,回判和检验...     

Shapley-模糊神经网络方法在华南台风卫星云图的长时效滚动预测中的应用

为了更好地利用大量的卫星云图观测资料来提高台风暴雨的预报能力,解决并提高对台风强降水云系变化的预报精度,延长对未来云系变化的预报时效,构建基于合作对策Shapley-模糊神经网络的华南区域台风卫星云图非线性智能计算滚动集合预测模型,对增强卫星云图资料在台风暴雨天气预报中的实用性和及时性具有重要意义.依据2013—201...