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.

A NEURAL NETWORK MODEL WITH FEATURE SELECTION FOR KOREAN SPEECH ACT CLASSIFICATION

2004 ◽  
Vol 14 (06) ◽  
pp. 407-414 ◽  
Author(s):  
KYUNGSUN KIM ◽  
HARKSOO KIM ◽  
JUNGYUN SEO
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Speech Act ◽  
Feature Selection Method ◽  
Selection Method ◽  
Morphological Features ◽  
Experimental Result ◽  
Real Field

A speech act is a linguistic action intended by a speaker. Speech act classification is an essential part of a dialogue understanding system because the speech act of an utterance is closely tied with the user's intention in the utterance. We propose a neural network model for Korean speech act classification. In addition, we propose a method that extracts morphological features from surface utterances and selects effective ones among the morphological features. Using the feature selection method, the proposed neural network can partially increase precision and decrease training time. In the experiment, the proposed neural network showed better results than other models using comparatively high-level linguistic features. Based on the experimental result, we believe that the proposed neural network model is suitable for real field applications because it is easy to expand the neural network model into other domains. Moreover, we found that neural networks can be useful in speech act classification if we can convert surface sentences into vectors with fixed dimensions by using an effective feature selection method.

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 A NEURAL NETWORK NOISE PREDICTION MODEL FOR TEHRAN URBAN ROADS

2018 ◽  
Vol 26 (2) ◽  
pp. 88-97 ◽  
Author(s):  
Ali Mansourkhaki ◽  
Mohammadjavad Berangi ◽  
Majid Haghiri ◽  
Mohammadreza Haghani
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Neural Network Model ◽  
Goodness Of Fit ◽  
Measurement Data ◽  
Reflection Factor ◽  
Road Traffic Noise ◽  
The Neural Network ◽  
Marquardt Algorithm

Over the last decades, the number of motor vehicles has increased dramatically in Iran, where different traffic characteristics and urban structures are notable. In the present study, a multilayer perceptron neural network model trained with the Levenberg-Marquardt algorithm was used for predicting the equivalent sound level (LAeq) originating from traffic. Fifty-one samples were collected from different areas of Tehran. Input parameters consisted of total traffic volume per hour, average speed of vehicles, percentage of each category of vehicles, road gradient, density of buildings around the road section and a new parameter named “Building Reflection Factor”. These data were randomly used with 80, 10 and 10 percentiles respectively for training, validation and testing of the Artificial Neural Network (ANN). Results yielded by the ANN model were compared with field measurement data, a proposed regression model and some classical well-known models. Our study indicated that the prediction error of the neural network model was much less than that of the regression model and other classical models. Moreover, a statistical t-test was applied for evaluating the goodness-of-fit of the proposed model and proved that the neural network model is highly efficient in estimating road traffic noise levels.

scholarly journals Tensorized Neural Network for Multi-Aspect Rating-Based Recommendation

2019 ◽  
Vol 8 (2S11) ◽  
pp. 547-551
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Activation Function ◽  
Research Area ◽  
Experimental Result ◽  
Computational Time ◽  
Local Optimum ◽  
Tensor Factorization ◽  
Value Decomposition

Generating personalized recommendations is one of the most crucial aspects in Recommender System research area. Most of the researches only focus on the accuracy of recommendation using collaborative filtering that relies on a single overall rating that represents the overall preferences. However, the user may have a different emphasis on different specific aspects that affect the users’ final rating decisions. Therefore, we present a neural network model that utilize multi-aspects ratings using Tensor Factorization to improve the accuracy of personalization, as well as optimizing the dynamic weights of the aspect. To measure the estimated weights for the aspects, we employ the Higher Order Singular Value Decomposition (HOSVD) technique called CANDECOMP/PARAFAC (CP) decomposition that allows for multi-faceted data processing. We then develop the Neural Network with backpropagation error to train the model with different parameter settings and limited computational time. We also use a non-linear activation function in each hidden layer in various settings. The experimental result measured using MAE shows that the proposed model has significantly outperformed the baseline approach in terms of the prediction accuracy. Based on the observation, the performance of rating prediction has been improved by employing the Tensorized Neural Network model and can overcome the problem of local optimum convergence for multi-aspect rating recommendation.

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.

scholarly journals A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hani Omar ◽  
Van Hai Hoang ◽  
Duen-Ren Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Experimental Result ◽  
Forecasting Model ◽  
Hybrid Neural Network ◽  
Prediction Result ◽  
Forecasting Method ◽  
Google Search

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.

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