Line Loss Rate Forecasting Based on Grey Model and Combination of Neural Network

2012 ◽  
Vol 621 ◽  
pp. 340-343 ◽  
Author(s):  
Fei Xie ◽  
Bu Xiang Zhou ◽  
Qin Zhang ◽  
Long Jiang
Keyword(s):  
Neural Network ◽  
Loss Rate ◽  
Grey Model ◽  
Combined Model ◽  
Related Factors ◽  
The Neural Network ◽  
Line Loss ◽  
Input Variables ◽  
Relationship Of ◽  
Bp Model

This thesis is mainly focusing on the research of the method for line loss rate forecast by adopting grey model combined with neural network. Firstly, GM(1,1) model can be used to analyze and calculate line loss rate change trend. The input variables of the neural network could be determined by grey relationship of related factors. Three-Layer BP model for line loss rate forecast is constructed, and then the eventual result can be obtained by using the combined model of GM(1,1) and neural network method. An example is taken to prove the precision improved for line loss rate forecast by the proposed method studied in the thesis.

scholarly journals Improving prediction of neural networks: a study of tow financial prediction tasks

2004 ◽  
Vol 8 (4) ◽  
pp. 219-233
Author(s):  
Tarun K. Sen ◽  
Parviz Ghandforoush ◽  
Charles T. Stivason
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Models ◽  
Prediction Performance ◽  
Financial Prediction ◽  
The Neural Network ◽  
Input Variables ◽  
Relationship Of

Neural networks are excellent mapping tools for complex financial data. Their mapping capabilities however do not always result in good generalizability for financial prediction models. Increasing the number of nodes and hidden layers in a neural network model produces better mapping of the data since the number of parameters available to the model increases. This is determinal to generalizabilitiy of the model since the model memorizes idiosyncratic patterns in the data. A neural network model can be expected to be more generalizable if the model architecture is made less complex by using fewer input nodes. In this study we simplify the neural network by eliminating input nodes that have the least contribution to the prediction of a desired outcome. We also provide a theoretical relationship of the sensitivity of output variables to the input variables under certain conditions. This research initiates an effort in identifying methods that would improve the generalizability of neural networks in financial prediction tasks by using mergers and bankruptcy models. The result indicates that incorporating more variables that appear relevant in a model does not necessarily improve prediction performance.

scholarly journals STUDY OF A MA16-BASED VERTEX TRIGGER FOR THE CDF EXPERIMENT

1995 ◽  
Vol 06 (05) ◽  
pp. 681-692
Author(s):  
R. ODORICO
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Impact Parameter ◽  
Electronic Components ◽  
The Neural Network ◽  
Viable Solution ◽  
Impact Parameters ◽  
Input Variables ◽  
Better Than

A Neural Network trigger for [Formula: see text] events based on the SVT microvertex processor of experiment CDF at Fermilab is presented. It exploits correlations among track impact parameters and azimuths calculated by the SVT from the SVX microvertex detector data. The neural trigger is meant for implementation on the systolic Siemens microprocessor MA16, which has already been used in a neural-network trigger for experiment WA92 at CERN. A suitable set of input variables is found, which allows a viable solution for the preprocessing task using standard electronic components. The response time of the neural-network stage of the trigger, including preprocessing, can be estimated ~10 μs. Its precise value depends on the quantitative specifications of the output signals of the SVT, which is still in development. The performance of the neural-network trigger is found to be significantly better than that of a conventional trigger exclusively based on impact parameter data.

An improvement to the ANN-enhanced flow rating method

2021 ◽  
Author(s):  
Bernhard Schmid
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Water Resources ◽  
Flow Rate ◽  
Major Ions ◽  
Rate Of Change ◽  
Data Set ◽  
Input Variables ◽  
Relationship Of ◽  
The Relationship

<p>The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain ‘better’, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author’s experience.</p><p>The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).</p><p>The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the Mödling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the Mödling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only.    </p><p> </p><p>References</p><p>Moatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.</p><p>Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.</p><p>Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.</p><p>Tunqui Neira, J.M., Andréassian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.</p><p>Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.</p>

Neural Network-Based Stock Market Return Forecasting Using Data Mining for Variable Reduction

2008 ◽  
pp. 2476-2493 ◽  
Author(s):  
David Encke
Keyword(s):  
Neural Network ◽  
Information Gain ◽  
Network Models ◽  
Linear Regression Models ◽  
Market Returns ◽  
Neural Network Models ◽  
The Neural Network ◽  
Variable Reduction ◽  
Input Variables ◽  
Using Data

Researchers have known for some time that nonlinearity exists in the financial markets and that neural networks can be used to forecast market returns. Unfortunately, many of these studies fail to consider alternative forecasting techniques, or the relevance of the input variables. The following research utilizes an information-gain technique from machine learning to evaluate the predictive relationships of numerous financial and economic input variables. Neural network models for level estimation and classification are then examined for their ability to provide an effective forecast of future values. A cross-validation technique is also employed to improve the generalization ability of the models. The results show that the classification models generate higher accuracy in forecasting ability than the buy-and-hold strategy, as well as those guided by the level-estimation-based forecasts of the neural network and benchmark linear regression models.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

Neural networks for modelling the energy consumption of metro trains

2019 ◽  
Vol 234 (7) ◽  
pp. 722-733 ◽  
Author(s):  
Pablo Martínez Fernández ◽  
Pablo Salvador Zuriaga ◽  
Ignacio Villalba Sanchís ◽  
Ricardo Insa Franco
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Mean Square Error ◽  
Mean Square ◽  
The Neural Network ◽  
Train Speed ◽  
Input Variables ◽  
Trained Neural Network ◽  
Traction Power

This paper presents the application of machine learning systems based on neural networks to model the energy consumption of electric metro trains, as a first step in a research project that aims to optimise the energy consumed for traction in the Metro Network of Valencia (Spain). An experimental dataset was gathered and used for training. Four input variables (train speed and acceleration, track slope and curvature) and one output variable (traction power) were considered. The fully trained neural network shows good agreement with the target data, with relative mean square error around 21%. Additional tests with independent datasets also give good results (relative mean square error = 16%). The neural network has been applied to five simple case studies to assess its performance – and has proven to correctly model basic consumption trends (e.g. the influence of the slope) – and to properly reproduce acceleration, holding and braking, although it tends to slightly underestimate the energy regenerated during braking. Overall, the neural network provides a consistent estimation of traction power and the global energy consumption of metro trains, and thus may be used as a modelling tool during further stages of research.

scholarly journals Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 381 ◽  
Author(s):  
Jong-Min Kim ◽  
Ning Wang ◽  
Yumin Liu ◽  
Kayoung Park
Keyword(s):  
Neural Network ◽  
Control Chart ◽  
Network Model ◽  
Neural Network Model ◽  
Multivariate Data ◽  
Principal Component ◽  
Binary Response ◽  
Link Functions ◽  
The Neural Network ◽  
Input Variables

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.

scholarly journals PV Module Fault Detection Using Combined Artificial Neural Network and Sugeno Fuzzy Logic

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2150
Author(s):  
Romênia G. Vieira ◽  
Mahmoud Dhimish ◽  
Fábio M. U. de Araújo ◽  
Maria I. S. Guerra
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Fault Detection ◽  
Machine Learning Techniques ◽  
Photovoltaic Systems ◽  
The Neural Network ◽  
Learning Techniques ◽  
Network Output ◽  
Input Variables ◽  
Power Point

This work introduces a new fault detection method for photovoltaic systems. The method identifies short-circuited modules and disconnected strings on photovoltaic systems combining two machine learning techniques. The first algorithm is a multilayer feedforward neural network, which uses irradiance, ambient temperature, and power at the maximum power point as input variables. The neural network output enters a Sugeno type fuzzy logic system that precisely determines how many faulty modules are occurring on the power plant. The proposed method was trained using a simulated dataset and validated using experimental data. The obtained results showed 99.28% accuracy on detecting short-circuited photovoltaic modules and 99.43% on detecting disconnected strings.

scholarly journals Hand–Eye Calibration Algorithm Based on an Optimized Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 85
Author(s):  
Jiang Hua ◽  
Liangcai Zeng
Keyword(s):  
Neural Network ◽  
Coordinate System ◽  
Calibration Method ◽  
Calibration Model ◽  
The Neural Network ◽  
Calibration Algorithm ◽  
Robot Grasping ◽  
Relationship Of ◽  
The Relationship ◽  
Camera Coordinate System

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B=fA, which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system.

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