4 edition of **A comparison of neural network and linear regression models for predicting El Nino events** found in the catalog.

A comparison of neural network and linear regression models for predicting El Nino events

- 98 Want to read
- 2 Currently reading

Published
**1994**
by U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Office of the Director, For sale by the National Technical Information Service in Silver Spring, Md, Springfield, VA
.

Written in English

- Climatic changes -- Forecasting,
- Ocean temperature -- Forecasting,
- El Niño Current -- Forecasting

**Edition Notes**

Statement | Robert P. Baptist ... [et al.] |

Series | NOAA technical memorandum ERL OD -- 21 |

Contributions | Baptist, Robert P, Environmental Research Laboratories (U.S.). Office of the Director |

The Physical Object | |
---|---|

Format | Microform |

Pagination | 1 v |

ID Numbers | |

Open Library | OL14452241M |

OCLC/WorldCa | 31455573 |

The use of NARX Neural Networks to predict Chaotic Time Series EUGEN DIACONESCU, PhD Electronics, Communications and Computer Science Faculty University of Pitesti Targu din Vale, Nr. 1 ROMANIA [email protected] Abstract: The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This. Neural Network: Linear Perceptron xo ∑ = w⋅x = i M i wi x 0 xi xM w o wi w M Input Units Output Unit Connection with weight Note: This input unit corresponds to the “fake” attribute xo = 1. Called the bias Neural Network Learning problem: Adjust the connection weights so that the network generates the correct prediction on the training File Size: KB.

sion models. Multiple regression models and neural net-works are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indi-cate that neural network techniques are somewhat (but not dramatically) better than regression models for daily. The three techniques selected where linear regression analysis (LRA), classification and regression trees (CART) and artificial neural networks (ANN). The analysis illustrates that when the relationship between the independent and dependent variables is a linear one, that LRA is by: 3.

In today’s global competitive environment, it is important to be able to evaluate the efficient use of a firms’ resources. The aim of this study is to predict the discard rate for headlight frames before the project of an automotive sub-industry firm in Bursa. For this prediction, the multilayer perceptron model, the radial basis function network model and multiple linear regression models Author: Vesile Sinem Arikan Kargi. Deep learning techniques such as convolutional neural networks (CNNs) can potentially provide powerful tools for classifying, identifying, and predicting patterns in Cited by: 3.

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A comparison of neural network and linear regression models for predicting El Nino events (SuDoc C /2:ERL OD) [U.S. National Archives and Records Administration] on *FREE* shipping on qualifying offers.

A comparison of neural network and linear regression models for predicting El Nino events (SuDoc C /2:ERL OD)Author: U.S.

National Archives and Records Administration. Neural networks are found to be comparable but not better than linear discriminant analysis in two-group two-variable problems. However, neural networks are found to perform better when either the number of groups or the number of variables increases and also when the classification task tends to become by: V.S.

Desai, and R. Bharati, A comparison of linear regression and neural network methods for predicting returns on asset classes, Proceedings of the National Meeting of the Decision Sciences Institute San Francisco, Cited by: The aim of this study is to compare the predictive performance of feed forward neural network with some of the regression models that are capable of handling certain nonlinear prediction problems.

Na-udom A., Rungrattanaubol J. () A Comparison of Artificial Neural Network and Regression Model for Predicting the Rice Production in Lower Northern Thailand. In: Kim K. (eds) Information Science and Applications.

Lecture Notes in Electrical Engineering, vol Springer, Berlin, Heidelberg. First Online 18 February Cited by: 1. Comparison of artificial neural network and regression models for prediction of performance of ANN model is compared with response surface model based on multiple non-linear regression analysis.

Linear density per filament, overfeed, air-pressure and texturing-speed have been selected as input variables as they of the neural network.

2 Cited by: 1. accidents. In addition, by introducing the linear regression model, Algorithm. First, this paper is a process of finding waves. It is related to this paper in that it predicts the predicted dangerous waves by applying linear regression algorithms[10]. Figure 1 shows the result of the linear regression.

Neural Network. between and MJ m-2 d-1 for linear regressions and neural networks, and coefficients of correlation (r2) between andrespectively.

Even though neural networks and linear regression models can be used to predict the daily global solar radiation appropriately, neural networks produced better estimates.

The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange. The Comparison of Methods Artificial Neural Network. with Linear Regression Using Specific Variables for. Prediction Stock Price in Tehran Stock Exchange.

Reza Gharoie Ahangar. 1 A Comparison of Linear Forecasting Models and Neural Networks: An Application to Euro inflation and Euro Divisia Short title: Linear Models versus Neural Networks in Macroeconomic Forecasting Jane M.

Binner (corresponding author) Department of Information Management and Systems. use of Artiﬁcial Neural Networks (ANNs) and Support Vector Machines (SVM) to build prediction models for the S&P stock index. We will also show how traditional models such as multiple linear regression (MLR) behave in this case.

The developed models will be evaluated and compared based on a number of evaluation criteria. Ayoubi et al. ([51]) In their study, designed artificial neural network (ANN) models to predict the biomass and grain yield of barley from soil properties; and they compared the performance ofANN models with earlier tested statistical models based on multivariate Size: KB.

The MATLAB Neural Network Toolbox was used for the training and optimization of the ANN model. The ANN model having the best prediction performance was detected by trying various networks.

Then, the ANN results were compared with the results of multiple linear regression (MLR) by: A comparison of neural network and linear regression models for predicting El Nino events Author: Robert P Baptist ; Environmental Research Laboratories (U.S.). Logistic regression with forward method and feed forward Artificial Neural Network with 15 neurons in hidden layer were fitted to the dataset.

The accuracy of the models in predicting academic failure was compared by using ROC (Receiver Operating Characteristic) and Cited by: 3. Three predictive models have been developed using SAS Enterprise Miner, that are, artificial neural network, decision tree and linear regression.

The result of this study shows that all of the. Neural Network, but compares the result with predication from Linear Regression and draws conclusion.

The Artificial neural network (ANN) is an intelligent tool developed for prediction, classification, optimization and other purposes. The ANN was developed to solve complex computational problems like natural neurons in living organisms.

and also to do an inter-comparison of the models. Non-linear regression analysis was done to critically analyze the usefulness of the ANN models while modeling the AIR with the single data set. That the ANN model is a better option than the linear regression model was observed. INTRODUCTION.

Regression and Neural Networks Models for. Prediction of Crop Production. 1 Raju Prasad Paswan, 2 Dr.(Mrs.) Shahin Ara Begum. Abstract- Neural networks have been gaining a great deal of importance since the last few years. They have been used in the areas of prediction and classification; the areas where regression and other statistical models.

This study develops a CNN-based statistical prediction system for the ENSO prediction. In this study, long-term Global Climate Model (GCM) output is utilized to train and validate the CNN model.

As variables for the input layer, SST anomaly and heat content anomaly for February, March and April are given, and used the Nino index of the subsequent DJF as a variable for the output : Jeong-Hwan Kim, Yoo-Geun Ham.

feed forward neural network is similar to a linear regression model. Likewise, if g and g are logistic activation functions, then the single hidden layer feed forward neural network is similar to logistic regression.

Because of this comparison between single hidden layer feed forwardCited by: comparison of various neural network models with traditional statistical methods.

Sarle [4] translates neural network jargon into statistical jargon, and shows the relationships between neural networks and statistical models such as generalized linear models, maximum redundancy analysis, projection pursuit, and cluster analysis.Variables Selection of Neural Networks.

To compare logistic regression (LR) and neural networks (NNs) models, many papers use the same variables for both input models (the variables selected by the multivariate analysis). This choice is justified by the large degree of overlap between the sets of variables selected with both by: