Medium Gaussian SVM, Wide Neural Network and Stepwise Linear method in estimation of Lornoxicam pharmaceutical solubility in supercritical solvent

2021 ◽  
pp. 118120
Author(s):  
Tao Wang ◽  
Chia-Hung Su
Keyword(s):  
Neural Network ◽  
Linear Method ◽  
Supercritical Solvent

Prediction of Construction Schedule Based on BP Neural Network

2013 ◽  
Vol 405-408 ◽  
pp. 3423-3428
Author(s):  
Zhao Lin Li ◽  
Guo Zhi Zhang
Keyword(s):  
Neural Network ◽  
Project Management ◽  
Bp Neural Network ◽  
Linear Method ◽  
Schedule Control ◽  
Construction Schedule

Schedule control is the major issue in project management, and to predict the construction schedule effectively is important practically. The article mainly predicts the schedule of a project based on BP neural network. The result shows that the predicted value is more accurate than the value calculated by linear method.

Early Warning of Gas Turbine Failure by Nonlinear Feature Extraction Using an Auto-Associative Neural Network Approach

2011 ◽  
Author(s):  
Thomas Palme´ ◽  
Peter Breuhaus ◽  
Mohsen Assadi ◽  
Albert Klein ◽  
Minkyo Kim
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Early Warning ◽  
Linear Method ◽  
Data Driven ◽  
Operational Parameters ◽  
Neural Network Approach ◽  
Data Set ◽  
Nonlinear Pca ◽  
Auto Associative Neural Network

This study investigates the application of nonlinear Principal Component Analysis (PCA), implemented through the use of Auto-Associative Neural Network (AANN), for early warning of impending gas turbine failure. The study is based on a real operational data set that includes a compressor failure. The analyzed data set consists of measured operational parameters whose identity are unknown, hence this study presents a purely data driven approach to the problem of early warning. In this case study, the use of AANNs for early detection of abnormal engine behavior could have provided the operator with a warning a few days prior to the fully developed failure, which resulted in a forced shut-down and extensive maintenance. Furthermore, a comparison is made between the nonlinear PCA by AANNs and the standard PCA model, which is an inherently linear method. The result shows that the AANN provides a more reliable detection of the failure by a higher residual generation during failure mode as well as fewer false indications prior to the failure. Consequently, this study shows that nonlinear PCA as performed with AANNs can be a valuable data driven tool for early warning of gas turbine failure.

scholarly journals Prognostic Validity of Statistical Prediction Methods Used for Talent Identification in Youth Tennis Players Based on Motor Abilities

2021 ◽  
Vol 11 (15) ◽  
pp. 7051
Author(s):  
Maximilian Siener ◽  
Irene Faber ◽  
Andreas Hohmann
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
High Performance ◽  
Linear Method ◽  
Statistical Prediction ◽  
Prediction Methods ◽  
Talent Identification ◽  
Tennis Players ◽  
Long Term Study ◽  
The Individual

(1) Background: The search for talented young athletes is an important element of top-class sport. While performance profiles and suitable test tasks for talent identification have already been extensively investigated, there are few studies on statistical prediction methods for talent identification. Therefore, this long-term study examined the prognostic validity of four talent prediction methods. (2) Methods: Tennis players (N = 174; n♀ = 62 and n♂ = 112) at the age of eight years (U9) were examined using five physical fitness tests and four motor competence tests. Based on the test results, four predictions regarding the individual future performance were made for each participant using a linear recommendation score, a logistic regression, a discriminant analysis, and a neural network. These forecasts were then compared with the athletes’ achieved performance success at least four years later (U13‒U18). (3) Results: All four prediction methods showed a medium-to-high prognostic validity with respect to their forecasts. Their values of relative improvement over chance ranged from 0.447 (logistic regression) to 0.654 (tennis recommendation score). (4) Conclusions: However, the best results are only obtained by combining the non-linear method (neural network) with one of the linear methods. Nevertheless, 18.75% of later high-performance tennis players could not be predicted using any of the methods.

scholarly journals Clustering and mapping spatial-temporal datasets using SOM neural networks

2003 ◽  
Vol 13 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Irini Reljin ◽  
Branimir Reljin ◽  
Gordana Jovanovic
Keyword(s):  
Neural Network ◽  
High Efficiency ◽  
Function Analysis ◽  
Linear Method ◽  
Nonlinear Phenomena ◽  
Self Organizing Map ◽  
Climate Data ◽  
Significant Similarity ◽  
Nonlinear Methods ◽  
Som Network

Large datasets can be analyzed through different linear and nonlinear methods. Most frequently used linear method Is Principal Component Analysis (PCA) known also as EOF (Empirical Orthogonal Function) analysis, permitting both clustering and visualizing high-dimensional data Items. However, many problems are nonlinear In nature, so, for analyzing such a problems some nonlinear methods will be more appropriate. The SOM (Self-Organizing Map) neural network is very promising tool for clustering and mapping spatial-temporal datasets describing nonlinear phenomena. The SOM network is applied on the precipitation and temperature data observed in the region of Serbia and Montenegro during 48 years period (1951-1998) and the zonal maps of homogeneous geographical units are derived. These maps are compared with those recently derived via EOF analysis. Significant similarity of results derived from the two methods confirms high efficiency of the SOM network in analyzing spatial-temporal fields. Moreover, the SOM neural network is more appropriate in analyzing climate data since both climate data and the SOM analyzing method are nonlinear in nature.

THE CLUSNET ALGORITHM AND TIME SERIES PREDICTION

1993 ◽  
Vol 04 (03) ◽  
pp. 247-255 ◽  
Author(s):  
W. HSU ◽  
L. S. HSU ◽  
M. F. TENORIO
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Architecture ◽  
Time Series Prediction ◽  
Clustering Algorithms ◽  
Linear Method ◽  
Trade Offs ◽  
Local Linear Method ◽  
On Line ◽  
On Line Learning

This paper describes a novel neural network architecture named ClusNet. This network is designed to study the trade-offs between the simplicity of instance-based methods and the accuracy of the more computational intensive learning methods. The features that make this network different from existing learning algorithms are outlined. A simple proof of convergence of the ClusNet algorithm is given. Experimental results showing the convergence of the algorithm on a specific problem is also presented. In this paper, ClusNet is applied to predict the temporal continuation of the Mackey–Glass chaotic time series. A comparison between the results obtained with ClusNet and other neural network algorithms is made. For example, ClusNet requires one-tenth the computing resources of the instance-based local linear method for this application while achieving comparable accuracy in this task. The sensitivity of ClusNet prediction accuracies on specific clustering algorithms is examined for an application. The simplicity and fast convergence of ClusNet makes it ideal as a rapid prototyping tool for applications where on-line learning is required.

An integrated system based on fuzzy genetic algorithm and neural networks for stock price forecasting

2014 ◽  
Vol 31 (3) ◽  
pp. 281-292 ◽  
Author(s):  
Noraddin Mousazadeh Abbassi ◽  
Mohammad Ali Aghaei ◽  
Mahdi Moradzadeh Fard
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Stock Market ◽  
Price Index ◽  
Stock Exchange ◽  
Linear Method ◽  
Integrated System ◽  
Content Type ◽  
Non Linear ◽  
Fuzzy Genetic

Purpose – The aim of this research is to predict the total stock market index of the Tehran Stock Exchange, using the compound method of fuzzy genetics and neural network, in order for the active participants of the finance market as well as macro decision makers to be able to predict the market trend. Design/methodology/approach – First, the prediction was done by neural network, then the output weight of optimum neural network was taken as standard to repeat this prediction using the genetic algorithm, and then the extracted pattern from the neural network was stated through discernible rules using fuzzy theory. Findings – The main attention of this paper is investors and traders to achieve a method for predicting the stock market. Concerning the results of previous research, which confirms the relative superiority of non-linear models in price index prediction, an appropriate model has been offered in this research by compounding the non-linear method such as fuzzy genetics and neural network. The results indicate superiority of the designed system in predicting price index of the Tehran Stock Exchange. Originality/value – This paper states its originality and value by compounding the non-linear method issues pattern to predict stock market, to encourage further investigation by academics and practitioners in the field.

scholarly journals Maximum and minimum temperature prediction over western Himalaya using artificial neural network

MAUSAM ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 283-290
Author(s):  
PIYUSH JOSHI ◽  
A. GANJU
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Minimum Temperature ◽  
Linear Method ◽  
Western Himalaya ◽  
Temperature Prediction ◽  
Data Set ◽  
Non Linear ◽  
Artificial Neural ◽  
Mean Square Errors

Due to eastward moving synoptic weather system called Western Disturbance (WD), Western Himalaya receives enormous amount of precipitation in the form of snow during winter months (November to April). This precipitation keeps on accumulating and poses an avalanche threat. Temperature plays an important role for the initiation of avalanches. Therefore, prediction of maximum and minimum temperature may be quite helpful for avalanche forecasting. In the present study Artificial Neural Network (ANN), a non-linear method is used for the prediction of maximum and minimum temperature using surface meteorological data observed at various observatories in Western Himalaya region. ANN provides a computational efficient way of determining an empirical possible non-linear relationship between a number of input and one or more outputs. In present study back propagation learning algorithm is used to train the network. In the training process the relationship between input and output is extracted i.e., final weights are computed. Past data of about 25 years is used for training the network and trained network is used for temperature prediction for five winter seasons (2005-06 to 2009-10). Root mean square errors (RMSE) corresponding to maximum and minimum temperature are computed. For independent data set RMSE vary from 2.18 to 2.48 and 1.99 to 2.78 for maximum and minimum temperatures respectively.

scholarly journals Moisture Prediction from Simple Micrometeorological Data

1999 ◽  
Vol 89 (8) ◽  
pp. 668-672 ◽  
Author(s):  
Y. Chtioui ◽  
L. J. Francl ◽  
S. Panigrahi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Flag Leaf ◽  
Linear Method ◽  
Regression Tree ◽  
Absolute Error ◽  
Generalized Regression Neural Network ◽  
Tree Model ◽  
Regression Methods ◽  
Plant Disease Management

Four linear regression methods and a generalized regression neural network (GRNN) were evaluated for estimation of moisture occurrence and duration at the flag leaf level of wheat. Moisture on a flat-plate resistance sensor was predicted by time, temperature, relative humidity, wind speed, solar radiation, and precipitation provided by an automated weather station. Dew onset was estimated by a classification regression tree model. The models were developed using micrometeorological data measured from 1993 to 1995 and tested on data from 1996 and 1997. The GRNN outperformed the linear regression methods in predicting moisture occurrence with and without dew estimation as well as in predicting duration of moisture periods. Average absolute error for prediction of moisture occurrence by GRNN was at least 31% smaller than that obtained by the linear regression methods. Moreover, the GRNN correctly predicted 92.7% of the moisture duration periods critical to disease development in the test data, while the best linear method correctly predicted only 86.6% for the same data. Temporal error distribution in prediction of moisture periods was more highly concentrated around the correct value for the GRNN than linear regression methods. Neural network technology is a promising tool for reasonably precise and accurate moisture monitoring in plant disease management.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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