Integration of artificial intelligence with meta classifiers for the gully erosion susceptibility assessment in Hinglo river basin,Eastern India

The main aim of this study is to evaluate the gully erosion susceptibility coupling the artificial intelligence and machine learning ensemble approaches. In the present study, the multilayer perceptron neural network (MLP) was used as the base classifier and the hybrid ensemble machine learning methods i.e. Bagging and Dagging were used as the functional classifiers. The Hinglo river basin, an important tributary of the Ajay River was selected as the study area, consists with the parts of Chhotonagpur plateau and Rarh lateritic region. The study area is facing the gully erosion problems which are interrupted the growth of the agriculture. The gully erosion susceptibility maps (GESMs), prepared by MLP, MLP-Bagging and MLP-Dagging were classified into four classes such as low, moderate, high and very high susceptibility classes with the help of natural break method (NBM) in GIS environment. The very high susceptibility class covered 19.41% (MLP), 13.52% (MLP-Bagging) and 15.30% (MLP-Dagging) areas of the basin. For the evaluation and comparison of the models, receiver operating characteristics (ROC), accuracy, mean absolute error (MAE) and root mean square error (RMSE) were applied. Overall, all the gully erosion susceptibility models were performed as excellent. Integration of hybrid ensemble models with MLP has increase the accuracy of the MLP models. Among these models MLP-Dagging has achieved the highest accuracy in compare to the other models. The importance of the selected factors in the present study was assessed by the Relief-F method. The results show that the soil type factor has the highest predictive performance. Sensitivity analysis also showed soil type as most important factor. The gully erosion susceptibility maps (GESMs) are considered as the efficient tool which could be used to take the necessary steps for mitigating and controlling the soil erosion problem and sustainable environmental management and development.     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

Adaptive fuzzy neural network control for a space manipulator in the presence of output constraints and input nonlinearities

Space manipulator is considered as one of the most promising technologies for future space activities owing to its important role in various on-orbit serving missions. In this paper, a novel adaptive fuzzy neural network (FNN) control scheme is proposed for the trajectory tracking control of an attitude-controlled free-flying space manipulator in the presence of output constraints and input nonlinearities. The parametric uncertainties and external disturbances are also taken into the consideration. First, a model-based controller is designed by using the barrier Lyapunov function (BLF) to prevent the position tracking errors from violating the predefined output constraints. Then, an adaptive FNN controller is designed by using two FNNs to compensate for the lumped uncertainties and input nonlinearities, respectively. Rigorous theoretical analysis for the semiglobal uniform ultimate boundedness of the whole closed-loop system is provided. The proposed adaptive FNN controller can guarantee the position and velocity tracking errors converge to the small neighborhoods about zero, while ensuring the position tracking errors within the output constraints even in the presence of input nonlinearities. To the best of the authors’ knowledge, there are relatively few existing controllers can achieve such excellent control performance in the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control scheme.     

State and disturbance estimation for test masses of drag-free satellites based on self-recurrent wavelet neural network

In recent years, the drag-free satellites have been widely used for some fundamental physical experiments, such as checking short-range effects of general relativity, geopotential determination and the exploration of static ocean current. And the space-borne detector of gravitational waves is one of the important applications for drag-free satellites in the future. In this study, the estimation of relative motion state and disturbance for test masses of drag-free satellite after release are researched. Firstly, the relative motion model between the test masses is established based on corresponding reference frames. Secondly, a self-recurrent wavelet neural network estimator is designed to estimate the pure gravitational relative motion state and disturbance for the test masses, and the sliding mode controller is used to transfer the relative motion state to the ideal state. Finally, the proposed estimation methods are verified by the simulation results.     

基于贯序正则极端学习机的时间序列预测及其应用

为实现对液压泵特征参数的在线预测,提出一种贯序正则极端学习机(SRELM),并研究了基于SRELM的预测方法.SRELM根据结构风险最小化原理实现网络训练,其网络权值可随新样本的逐次加入而递推求解,具有泛化能力强与训练速度快的优点,因此适于特征参数的在线预测.基于SRELM的预测方法利用特征参数训练SRELM模型,以逐...     

New Neural Network Response Surface Methods for Reliability Analysis

This article presents two new kinds of artificial neural network (ANN) response surface methods (RSMs): the ANN RSM based on early stopping technique (ANNRSM-1), and the ANN RSM based on regularization theory (ANNRSM-2). The following improvements are made to the conventional ANN RSM (ANNRSM-0): 1) by monitoring the validation error during the training process, ANNRSM-1 determines the early stopping point and the training stopping point, and the weight vector at the early stopping point, which corresponds to the ANN model with the optimal generalization, is finally returned as the training result; 2) according to the regularization theory, ANNRSM-2 modifies the conventional training performance function by adding to it the sum of squares of the network weights, so the network weights are forced to have smaller values while the training error decreases. Tests show that the performance of ANN RSM becomes much better due to the above-mentioned improvements: first, ANNRSM-1 and ANNRSM-2 approximate to the limit state function (LSF) more accurately than ANNRSM-0; second, the estimated failure probabilities given by ANNRSM-1 and ANNRSM-2 have smaller errors than that obtained by ANNRSM-0; third, compared with ANNRSM-0, ANNRSM-1 and ANNRSM-2 require much fewer data samples to achieve stable failure probability results.     

基于BP神经网络的金属橡胶本构关系的预估方法

采用BP(back propagation)神经网络算法对金属橡胶的本构关系系数进行学习训练,对BP神经网络做了比较详细的介绍,讨论了如何运用神经网络去预估金属橡胶的非线性本构关系系数,选择了BP神经网络参量,通过对金属橡胶的静态压缩实验数据进行参数识别,获得仅材料密度变化、材料密度和形状因子两种因素同时变化两种情况的...     

涡轴发动机自适应混合诊断模型高斯加权聚类方法

针对整个飞行包线内涡轴发动机健康参数估计问题,提出基于高斯加权聚类的机载自适应混合模型建立方法.机载自适应混合模型由卡尔曼滤波器和神经网络组成,由于飞行数据样本庞大,采用高斯加权模型对涡轴发动机飞行数据进行实时聚类,利用聚类数据更新神经网络权重,并实现自适应混合模型工作范围的自动扩展.仿真结果表明了该方法的有效性,采用...     

先进战斗机的鲁棒自适应反步控制

为解决反步法在虚拟控制求导时存在计算膨胀的缺陷问题,提出了一种自适应反步控制算法.采用CMAC神经网络在线学习系统不确定性以及各阶虚拟控制量的导数信息,从而避免了系统阶次较高时引起的计算膨胀问题,并且该控制算法在实际控制输入前加入低通滤波器,避免了不连续输入可能引起的抖振问题.Lyapunov稳定性分析表明,该控制方法...     

基于神经网络干扰观测器的柔性航天器姿态稳定控制

本文针对柔性航天器在惯性参数未知、外界干扰、输入饱和等复杂条件下的姿态控制问题,提出了1种基于神经网络干扰观测器的柔性航天器姿态稳定控制方法。首先,基于包含压电振动抑制输入的柔性航天器姿态动力学模型,构建了包含外界干扰、惯性参数不确定性的综合扰动项;其次,基于RBF神经网络设计干扰观测器与自适应参数调节律实时地估计综合扰动;再次,设计了1种固定时间收敛且有限时间稳定的非线性滑模控制器,并通过Lyapunov理论进行了稳定性分析;最后,利用航天器闭环姿态动力学系统进行数值仿真。结果表明:所设计的基于神经网络干扰观测器的控制方法可以有效实现航天器的姿态稳定、振动抑制与干扰估计,从而顺利完成航天器的高精高稳控制任务。