A Levenberg-Marquardt Neural Network Model with Rough Set for Protecting Citrus from Frost Damage

2012 ◽  
Author(s):  
Wei Zeng ◽  
Zili Zhang ◽  
Chao Gao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Rough Set ◽  
Neural Network Model ◽  
Frost Damage ◽  
Levenberg Marquardt

Study on the Levenberg-Marquardt Neural Network Model for Rock Rheology

2011 ◽  
Vol 71-78 ◽  
pp. 4103-4108
Author(s):  
Yu Zhou Jiang ◽  
Rui Hong Wang ◽  
Jie Bing Zhu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Testing Machine ◽  
Experimental Result ◽  
Left Bank ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Rheological Experiments ◽  
Rock Rheology

Rheological experiments were carried out for sandstone and marble specimens from left bank high slope of Jingping First Stage Hydropower Project by using the rock servo-controlling rheology testing machine. Typical triaxial rheological curves under step loading and temperature curves in the process of rheological experiment were gained. BP neural network is improved by Levenberg-Marquardt algorithm. Improved neural network model for rock rheology is established in accordance with the rheology experimental results of rock specimen. The improved neural network model was used to forecast rock rheological experimental curves, and the result shows that the forecasted rock rheology curves are closely accorded with the experimental result. The improved neural network model takes into account the influence of loading history and temperature difference on the rock rheological deformation, and the forecasted result can reflect better the rheology deformation behavior of rock material.

scholarly journals NEURAL NETWORKS FOR THE PREDICTION OF FRESH PROPERTIES AND COMPRESSIVE STRENGTH OF FLOWABLE CONCRETE

2019 ◽  
pp. 183-197
Author(s):  
Revathy Jayaseelan ◽  
Gajalskshmi Pandulu ◽  
Ashwini G
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Network Model ◽  
Neural Network Model ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Fresh Properties ◽  
Levenberg Marquardt ◽  
Oxide Particles ◽  
Quasi Newton

This paper presents the prediction of fresh concrete properties and compressive strength of flowable concrete through neural network approach. A comprehensive data set was generated from the experiments performed in the laboratory under standard conditions. The flowable concrete was made with two different types of micro particles and with single nano particles. The input parameter was chosen for the neural network model as cement, fine aggregate, coarse aggregate, superplasticizer, water-cement ratio, micro aluminium oxide particles, micro titanium oxide particles, and nano silica. The output parameter includes the slump Flow, L-Box flow, V Funnel flow and compressive strength of the flowable concrete. To develop a suitable neural network model, several training algorithms were used such as BFGS Quasi- Newton back propagation, Fletcher-Powell conjugate gradient back propagation, Polak - Ribiere conjugate gradient back propagation, Gradient descent with adaptive linear back propagation and Levenberg-Marquardt back propagation. It was found that BFGS Quasi- Newton back propagation and Levenberg-Marquardt back propagation algorithm provides more than 90% on the prediction accuracy. Hence, the model performance was agreeable for prediction purposes for the fresh properties and compressive strength of flowable concrete.

scholarly journals Internet of Things-Based Agricultural Mechanization Using Neural Network Extreme Learning on Rough Set

2021 ◽  
Vol 12 (2) ◽  
pp. 15-29
Author(s):  
Jian Chen ◽  
Xiaohua Chen ◽  
Qingyan Zeng ◽  
Ishbir Singh ◽  
Amit Sharma
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Network Model ◽  
Rough Set ◽  
Neural Network Model ◽  
Index System ◽  
Rough Set Theory ◽  
Agricultural Mechanization ◽  
The Neural Network ◽  
Before And After

Recently, the basic functioning of monitoring in internet of things (IoT) is to apply the monitored data to the database for the regular analysis through mobile or computer platform. The purpose of this article is to highlight the application scope of IoT knowledge and to present the model of agricultural IoT for prediction by studying the influence of IoT technology towards modern agriculture. In order to explore the uncertain characteristics of the development of agricultural mechanization, the evaluation index system is simplified through the existing rough set theory. The neural network model is established with five random provinces and cities in 31 provinces and municipalities as test samples. By comparing the data of the neural network model established before and after the reduction, the results show that the index coefficient is reduced by about 60% based on the fixed information before and after the reduction. The simulation evaluation accuracy established by the artificial neural network model is 100%, which is consistent with the results of the original index system.

scholarly journals Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Shipra Banik ◽  
A. F. M. Khodadad Khan ◽  
Mohammad Anwer
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Stock Market ◽  
Network Model ◽  
Rough Set ◽  
Neural Network Model ◽  
Stock Exchange ◽  
Machine Learning Techniques ◽  
Applied Machine Learning ◽  
The Neural Network

Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

scholarly journals Artificial Neural Network Model in Prediction of Meteorological Parameters during Premonsoon Thunderstorms

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
A. J. Litta ◽  
Sumam Mary Idicula ◽  
U. C. Mohanty
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Meteorological Parameters ◽  
Weather Prediction ◽  
Severe Thunderstorms ◽  
Levenberg Marquardt ◽  
Artificial Neural

Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent nonlinearity of their dynamics and physics. Accurate forecasting of severe thunderstorms is critical for a large range of users in the community. In this paper, experiments are conducted with artificial neural network model to predict severe thunderstorms that occurred over Kolkata during May 3, 11, and 15, 2009, using thunderstorm affected meteorological parameters. The capabilities of six learning algorithms, namely, Step, Momentum, Conjugate Gradient, Quick Propagation, Levenberg-Marquardt, and Delta-Bar-Delta, in predicting thunderstorms and the usefulness for the advanced prediction were studied and their performances were evaluated by a number of statistical measures. The results indicate that Levenberg-Marquardt algorithm well predicted thunderstorm affected surface parameters and 1, 3, and 24 h advanced prediction models are able to predict hourly temperature and relative humidity adequately with sudden fall and rise during thunderstorm hour. This demonstrates its distinct capability and advantages in identifying meteorological time series comprising nonlinear characteristics. The developed model can be useful in decision making for meteorologists and others who work with real-time thunderstorm forecast.

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