An artificial neural network model for estimating crop yields using remotely sensed information

Crop yield forecasting is a very important task for researchers in remote sensing. Problems exist with traditional statistical modelling (especially regression models) of nonlinear functions with multiple factors in the cropland ecosystem. This paper describes the successful application of an artificial neural network in developing a model for crop yield forecasting using back-propagation algorithms. The model has been adapted and calibrated using on the ground survey and statistical data, and it has proven to be stable and highly accurate.     

基于支持向量回归机的公路货运量预测模型*

为了提高公路货运量预测的能力,应用基于结构风险最小化准则的标准支持向量回归机方法来研究公路货运量预测问题.在选择适当的参数和核函数的基础上,通过对成都公路货运量时间序列进行预测,并与人工神经网络、线性回归分析等方法进行了对比,发现该方法能获得最小的训练相对误差和测试相对误差.     

An Adaptive Approach to Market Development Forecasting

In the field of time series models for forecasting, the commonly accepted fact is that no one model could be shown to be superior to all others. An effective time series model for forecasting must incorporate the specific characteristics of the targeted problem domain. This paper proposes a neura network model for market development forecasting In this model, monotonicity and knowledge of seasonal period are incorporated into neural network training. The model is superior to the traditional curve fitting methods, in that it is adaptive in modelling trend and season factors for the time series in cases where the growth curve functions and seasonal functions are a priori unknown. The model is superior to unconstrained neural networks for time series modelling in that random fluctuations can be avoided. An example of forecasting daily sales using the neural network model is demonstrated.     

The principles and practice of time series forecasting and business modelling using neural nets

This paper is intended as a hands-on practical discussion of how and why neural networks are used in forecasting and business modelling. The need for forecasting is briefly examined. The theory of the multilayer perceptron neural network is then covered both qualitatively and in mathematical detail, including the methods of back-propagation of error and independent validation. The advantages of the neural net approach to forecasting, namely nonlinear modelling capability, plausible interpolations and extrapolations, robustness to noise, ill-conditioning and insufficient data, and ease of use, are discussed. Finally, some working notes are offered for the practical implementation of neural nets in forecasting, and four real-life examples are given from the pursuits of econometrics, sales forecasting, market modelling, and risk evaluation.     

A novel method of short-term load forecasting based on multiwavelet transform and multiple neural networks

This paper aims to develop a load forecasting method for short-term load forecasting based on multiwavelet transform and multiple neural networks. Firstly, a variable weight combination load forecasting model for power load is proposed and discussed. Secondly, the training data are extracted from power load data through multiwavelet transform. Lastly, the obtained data are trained through a variable weight combination model. BP network, RBF network and wavelet neural network are adopted as the training network, and the trained data from three neural networks are input to a three-layer feedforward neural network for the load forecasting. Simulation results show that accuracy of the combination load forecasting model proposed in the paper is higher than any one single network model and the combination forecast model of three neural networks without preprocessing method of multiwavelet transform.     

Visualization of Neural Network Predictions for Weather Forecasting

Recurrent neural networks are prime candidates for learning evolutions in multi‐dimensional time series data. The performance of such a network is judged by the loss function, which is aggregated into a scalar value that decreases during training. Observing only this number hides the variation that occurs within the typically large training and testing data sets. Understanding these variations is of highest importance to adjust network hyper‐parameters, such as the number of neurons, number of layers or to adjust the training set to include more representative examples. In this paper, we design a comprehensive and interactive system that allows users to study the output of recurrent neural networks on both the complete training data and testing data. We follow a coarse‐to‐fine strategy, providing overviews of annual, monthly and daily patterns in the time series and directly support a comparison of different hyper‐parameter settings. We applied our method to a recurrent convolutional neural network that was trained and tested on 25 years of climate data to forecast meteorological attributes, such as temperature, pressure and wind velocity. We further visualize the quality of the forecasting models, when applied to various locations on the Earth and we examine the combination of several forecasting models.     

Forecasting warranty performance in the presence of the ‘maturing data’ phenomenon

Forecasting of warranty performance helps car engineers to fine-tune their strategies for warranty cost reduction. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at a certain future time, but also future MIS values. However, the ‘maturing data’ phenomenon that causes a warranty performance measure at specific MIS values to change with time make such forecasting challenging. Although dynamic linear models have been used for forecasting warranty performance, the focus mainly has been to utilize previous-model-year vehicle data for the analysis. In this paper, we apply a neural network model to forecast year-end warranty performance in the presence of the ‘maturing data’ phenomenon. We use a special type of neural network, viz. radial basis function (RBF), and optimize its parameters by minimizing training and testing errors through planned experimentation. This application shows the effectiveness of RBF neural networks to forecast warranty performance in the presence of the ‘maturing data’ phenomenon.     

基于预报-校正法的汇率预测模型

神经网络已成为金融时间序列预测的一个有力工具，但有些设计因素对神经网络的预测效果有很大的影响，这些因素包括输入变量选择、网络的结构和训练数据量。提出了基于预报一校正方法的神经网络预测模型，并对不同大小的训练集的影响进行了实验研究。结果发现大的训练集有更好的预测效果，且该方法的预测精度要普遍高于单一神经网络所能达到的效果。     

Neural networks of data inhibiting long memory pattern

We experiment with three neural network models for forecasting to better understand the performance of neural networks for the case when the data exhibits a long memory pattern. To obtain the optimum networks, the effect of network characteristics such as the training parameters, the number of hidden layers, and the testing and training percentages are simulated. The third model, which consists of a combination of individual time series forecasts, provides superior results.     

Hybrid neural network models for hydrologic time series forecasting

The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.     

基于遗传程序设计优化神经网络激励函数问题研究

近年来数据建模问题在数据挖掘、预测等领域得到广泛应用;神经网络由于其固有的许多优点,已成为解决很多问题的得力工具,对更深入探索非线性等现象起到了重大作用.如何根据问题建立一个好的神经网络是摆在我们面前最棘手的问题.利用遗传程序设计对神经网络激励函数进行优化,实验验证,通过此方法能更快学习到更适合问题解的神经网络.     

基于深度学习的遥感图像茶园区域识别应用研究

得益于遥感技术的发展和深度学习在图像处理方面的进展,采用深度学习识别遥感图像的方法被广泛应用。与传统的统计农作物种植面积方法相比较,通过深度学习的方法来识别茶园种植区域,可以减少人工依赖,节约人力资源,实时获取数据,具有更高的时效性。数据来源于Bigmap,以贵州省卫星遥感图像为数据基础,提出了使用深度学习来识别茶园区域的应用方法。实验目标为从整张遥感图像中提取出茶园种植区域。首先对遥感图像进行数据预处理,然后采用人工目视解译的方法标注出茶园区域并制成数据集,将数据集导入神经网络进行训练获得网络模型,最后将验证图像放入到训练好的神经网络当中,获得验证结果;检测精确率为95.83%,检测召回率为85.00%。     

An investigation of neural networks for linear time-series forecasting

This study examines the capability of neural networks for linear time-series forecasting. Using both simulated and real data, the effects of neural network factors such as the number of input nodes and the number of hidden nodes as well as the training sample size are investigated. Results show that neural networks are quite competent in modeling and forecasting linear time series in a variety of situations and simple neural network structures are often effective in modeling and forecasting linear time series.Scope and purposeNeural network capability for nonlinear modeling and forecasting has been established in the literature both theoretically and empirically. The purpose of this paper is to investigate the effectiveness of neural networks for linear time-series analysis and forecasting. Several research studies on neural network capability for linear problems in regression and classification have yielded mixed findings. This study aims to provide further evidence on the effectiveness of neural network with regard to linear time-series forecasting. The significance of the study is that it is often difficult in reality to determine whether the underlying data generating process is linear or nonlinear. If neural networks can compete with traditional forecasting models for linear data with noise, they can be used in even broader situations for forecasting researchers and practitioners.     

Application of feedforward neural network in the study of dissociated gas flow along the porous wall

This paper concerns the use of feedforward neural networks (FNN) for predicting the nondimensional velocity of the gas that flows along a porous wall. The numerical solution of partial differential equations that govern the fluid flow is applied for training and testing the FNN. The equations were solved using finite differences method by writing a FORTRAN code. The Levenberg–Marquardt algorithm is used to train the neural network. The optimal FNN architecture was determined. The FNN predicted values are in accordance with the values obtained by the finite difference method (FDM). The performance of the neural network model was assessed through the correlation coefficient (r), mean absolute error (MAE) and mean square error (MSE). The respective values of r, MAE and MSE for the testing data are 0.9999, 0.0025 and 1.9998 · 10^?5.     

基于t分布的贝叶斯深度学习模型及其应用

贝叶斯深度学习(BDL)融合了贝叶斯方法与深度学习(DL)的互补优势, 成为复杂问题中不确定性建模与推断的强大工具. 本文构建了基于t 分布和循环随机梯度汉密尔顿蒙特卡罗采样算法的BDL框架, 并基于数据不确定性和模型定不确定性给出了不确定性的度量. 为了验证模型框架的有效性和适用性, 我们分别基于人工神经网络(ANN)、卷积神经网络(CNN) 和循环神经网络(RNN)构建了相应的BDL模型, 并将模型应用于全球15个股票指数预测, 实证结果显示: 1)该框架在ANN、CNN和RNN 下均适用, 对全部指数的预测效果均很出色; 2) 在预测精度和通用性方面, 基于t分布BDL的模型比基于正态分布的BDL模型具有显著优越性; 3)在给定不确定性阈值之下的预测MAE 比初始MAE显著提升, 表明文中定义的不确定性是有效的, 对不确定性建模具有重要意义. 鉴于该BDL框架在预测精度、易于拓展和具备提供预测不确定性度量的优势, 其在金融和其他具有复杂数据特征的领域均有广阔的应用前景.     

Short-term electric power load forecasting using feedforward neural networks

Abstract: This paper presents the results of a study on short‐term electric power load forecasting based on feedforward neural networks. The study investigates the design components that are critical in power load forecasting, which include the selection of the inputs and outputs from the data, the formation of the training and the testing sets, and the performance of the neural network models trained to forecast power load for the next hour and the next day. The experiments are used to identify the combination of the most significant parameters that can be used to form the inputs of the neural networks in order to reduce the prediction error. The prediction error is also reduced by predicting the difference between the power load of the next hour (day) and that of the present hour (day). This is a promising alternative to the commonly used approach of predicting the actual power load. The potential of the proposed method is revealed by its comparison with two existing approaches that utilize neural networks for electric power load forecasting.     

Modelling and prediction using GMDH networks of Adalines with nonlinear preprocessors

This paper presents the results of an investigation into the use of GMDH neural networks for system modelling and prediction. A number of Adalines with nonlinear preprocessors were trained to yield a GMDH neural network. The training was carried out by applying the Widrow-Hoff learning rule. The structure of the network, i.e. the number of layers and the number of Adalines in each layer, was determined during training. The results obtained have shown that this type of network can successfully be employed for various system modelling and prediction tasks     

Time series forecasting based on wavelet decomposition and feature extraction

Time series forecasting is one of the most important issues in numerous applications in real life. The objective of this study was to propose a hybrid neural network model based on wavelet transform (WT) and feature extraction for time series forecasting. The motivation of the proposed model, which is called PCA-WCCNN, is to establish a single simplified model with shorter training time and satisfactory forecasting performance. This model combines the principal component analysis (PCA) and WT with artificial neural networks (ANNs). Given a forecasting sequence, order of the original forecasting model is determined firstly. Secondly, the original time series is decomposed into approximation and detail components by employing WT technique. Then, instead of using all the components as inputs, feature inputs are extracted from all the sub-series obtained from the above step. Finally, based on the extracted features and all the sub-series, a famous neural network construction method called cascade-correlation algorithm is applied to train neural network model to learn the dynamics. As an illustration, the proposed model is compared with two classical models and two hybrid models, respectively. They are the traditional cascade-correlation neural network, back-propagation neural network, wavelet-based cascade-correlation network using all the wavelet components as inputs to establish one model (WCCNN) and wavelet-based cascade-correlation network with combination of each sub-series model (WCCNN multi-models). Results obtained from this study indicate that the proposed method improves the accuracy of ANN and can yield better efficiency than other four neural network models.

     

Short‐term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation

This article presents the results of a study aimed at the development of a system for short‐term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural networks to predict future power demand based on past power load data and weather conditions. This study indicates that both neural network models exhibit comparable performance when tested on the training data but cosine RBF neural networks generalize better since they outperform considerably FFNNs when tested on the testing data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 591–605, 2005.     

Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks

Hybrid models such as the Artificial Neural Network-Autoregressive Integrated Moving Average (ANN–ARIMA) model are widely used in forecasting. However, inaccuracies and inefficiency remain in evidence. To yield the ANN–ARIMA with a higher degree of accuracy, efficiency and precision, the bootstrap and the double bootstrap methods are commonly used as alternative methods through the reconstruction of an ANN–ARIMA standard error. Unfortunately, these methods have not been applied in time series-based forecasting models. The aims of this study are twofold. First, is to propose the hybridization of bootstrap model and that of double bootstrap mode called Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (B-ANN–ARIMA) and Double Bootstrap Artificial Neural Network-Autoregressive Integrated Moving Average (DB-ANN–ARIMA), respectively. Second, is to investigate the performance of these proposed models by comparing them with ARIMA, ANN and ANN–ARIMA. Our investigation is based on three well-known real datasets, i.e., Wolf’s sunspot data, Canadian lynx data and, Malaysia ringgit/United States dollar exchange rate data. Statistical analysis on SSE, MSE, RMSE, MAE, MAPE and VAF is then conducted to verify that the proposed models are better than previous ARIMA, ANN and ANN–ARIMA models. The empirical results show that, compared with ARIMA, ANNs and ANN–ARIMA models, the proposed models generate smaller values of SSE, MSE, RMSE, MAE, MAPE and VAF for both training and testing datasets. In other words, the proposed models are better than those that we compare with. Their forecasting values are closer to the actual values. Thus, we conclude that the proposed models can be used to generate better forecasting values with higher degree of accuracy, efficiency and, precision in forecasting time series results becomes a priority.