Hybrid Neural Network for Modeling Automotive Clutches

1999 ◽  
Author(s):  
V. Parvataneni ◽  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. Tobler
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Hybrid Network ◽  
Hybrid Neural Network ◽  
Physical Knowledge ◽  
The Neural Network ◽  
Clutch Engagement ◽  
Wet Clutch ◽  
Engagement Process ◽  
Artificial Neural Network Ann

Abstract In this paper, artificial neural network (ANN) based models to capture the dynamic engagement torque of a wet clutch system are developed and analyzed. A two-layer recurrent ANN with output feedback is chosen as the baseline architecture since its simplicity allows easy implementation and efficient execution. Although this model exhibits good performance in capturing the overall mean level of the engagement torque as a function of time, it is unable to predict some of the important clutch dynamics behaviors. To improve the performance, additional neurons that represent the first principles of the clutch engagement process are incorporated into the neural network model. In other words, a hybrid model in which physical knowledge is implicitly embedded within the ANN architecture is derived. This hybrid model is trained and tested with experimental data. The results show that the performance of the hybrid network is much superior to that of the baseline ANN. It can successfully capture not only the trends, but also the detailed characteristics of the clutch engagement torque as a function of time.

Neural Networks in the Ice-Correction of Discharge Observations

1997 ◽  
Vol 28 (4-5) ◽  
pp. 283-296 ◽  
Author(s):  
Markus Huttunen ◽  
Bertel Vehviläinen ◽  
Esko Ukkonen
Keyword(s):  
Neural Network ◽  
Conceptual Model ◽  
Hybrid Model ◽  
Expert Knowledge ◽  
Time Estimation ◽  
Forecast Model ◽  
Hybrid Network ◽  
Physical Description ◽  
Backwater Effect ◽  
The Neural Network

We have applied three models, a neural network, a conceptual model and a combination of these two a hybrid model, to model the backwater effect of ice in a river. The neural network is a black-box model. It is based mainly on observed data and it lacks the expert knowledge of the system. The conceptual model is based on a physical description of the system. The data is used in optimizing the free parameters of the description. In the hybrid model, the neural network is modified so that the physical description of the conceptual model can be coded into the structure of the network. In the beginning of fitting, the hybrid network already performs as well as the conceptual model. During fitting also the structure of the physical description is optimized, not only the parameters of the description. The three models are rather different in form but in the modeling results there are only slight differences. Mean error of the models in ice-correction is 13-15 m3/s at an observation station where the mean backwater effect of the ice is 100 m3/s. The aim of this work is to develop a model for real time estimation of corrected discharge, which is used in error correction of a discharge forecast model. For this purpose the error of the best model is acceptable.

Modeling and experimental research on engaging characteristics of wet clutch

2019 ◽  
Vol 71 (1) ◽  
pp. 94-101 ◽  
Author(s):  
Yanzhong Wang ◽  
Yuan Li ◽  
Yang Liu ◽  
Wei Zhang
Keyword(s):  
Contact Surface ◽  
Hydrodynamic Lubrication ◽  
Load Capacity ◽  
Lubricating Oil ◽  
Centrifugal Effect ◽  
Element Analysis ◽  
Content Type ◽  
Clutch Engagement ◽  
Wet Clutch ◽  
Engagement Process

PurposeTo gain in-depth understandings of engaging characteristics, the purpose of this paper is to improve the model of wet clutches to predict the transmitted torque during the engagement process.Design/methodology/approachThe model of wet clutch during the engagement process took main factors into account, such as the centrifugal effect of lubricant, permeability of friction material, slippage factor of lubricant on contact surface and roughness of contact surface. Reynolds’ equation was derived to describe the hydrodynamic lubrication characteristics of lubricant film between the friction plate and the separated plate, and an elastic-plastic model of the rough surfaces contact based on the finite element analysis was used to indicate the loading force and friction torque of the contact surface.FindingsThe dynamic characteristics of wet clutch engagement time, relative speed, hydrodynamic lubrication of lubricating oil, rough surface contact load capacity and transfer torque can be obtained by the wet clutch engagement model. And the influence of the groove shape and depth on the engaging characteristics is also analyzed.Originality/valueThe mathematical model of the wet clutch during the engagement process can be used to predict the engaging characteristics of the wet clutch which could be useful to the design of the wet clutch.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Neural Networks Applied to Estimating Subglacial Topography and Glacier Volume

2009 ◽  
Vol 22 (8) ◽  
pp. 2146-2160 ◽  
Author(s):  
Garry K. C. Clarke ◽  
Etienne Berthier ◽  
Christian G. Schoof ◽  
Alexander H. Jarosch
Keyword(s):  
Neural Network ◽  
Regional Scale ◽  
Ice Thickness ◽  
Ice Dynamics ◽  
Ice Caps ◽  
Mountain Glaciers ◽  
The Neural Network ◽  
Glacier Ice ◽  
Subglacial Topography ◽  
Artificial Neural Network Ann

Abstract To predict the rate and consequences of shrinkage of the earth’s mountain glaciers and ice caps, it is necessary to have improved regional-scale models of mountain glaciation and better knowledge of the subglacial topography upon which these models must operate. The problem of estimating glacier ice thickness is addressed by developing an artificial neural network (ANN) approach that uses calculations performed on a digital elevation model (DEM) and on a mask of the present-day ice cover. Because suitable data from real glaciers are lacking, the ANN is trained by substituting the known topography of ice-denuded regions adjacent to the ice-covered regions of interest, and this known topography is hidden by imagining it to be ice-covered. For this training it is assumed that the topography is flooded to various levels by horizontal lake-like glaciers. The validity of this assumption and the estimation skill of the trained ANN is tested by predicting ice thickness for four 50 km × 50 km regions that are currently ice free but that have been partially glaciated using a numerical ice dynamics model. In this manner, predictions of ice thickness based on the neural network can be compared to the modeled ice thickness and the performance of the neural network can be evaluated and improved. From the results, thus far, it is found that ANN depth estimates can yield plausible subglacial topography with a representative rms elevation error of ±70 m and remarkably good estimates of ice volume.

Artificial Neural Networks and Learning Techniques

2016 ◽  
pp. 89-112
Author(s):  
Pushpendu Kar ◽  
Anusua Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Work ◽  
The Neural Network ◽  
Learning Techniques ◽  
Learning Procedure ◽  
Processing Component ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

scholarly journals PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6512
Author(s):  
Mario Tovar ◽  
Miguel Robles ◽  
Felipe Rashid
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Lasso Regression ◽  
Neural Network Models ◽  
Model Case ◽  
Hybrid Neural Network ◽  
Photovoltaic Power

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression.

ANN-Based Wheat Chlorophyll Density Estimation Using Canopy Hyperspectral Vegetation Indices

2012 ◽  
Vol 500 ◽  
pp. 243-249
Author(s):  
Da Cheng Wang ◽  
Luo Rui Sen ◽  
Ji Hua Wang ◽  
Cun Jun Li ◽  
Dong Yan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Statistical Method ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Leaf Chlorophyll ◽  
The Neural Network ◽  
Spectral Vegetation Indices ◽  
Artificial Neural Network Ann ◽  
Traditional Statistical Method

Canopy leaf Chlorophyll Density is a key index for evaluating crop potential photosynthetic efficiency and nutritional stress. Leaf Chlorophyll Density estimate using canopy hyperspectral vegetation indices provides a rapid and non-destructive method to evaluate yield predictions. A systematic comparison of two approaches to estimate Chlorophyll Density using 6 spectral vegetation indices (VIs) was presented in this study. In this study, the traditional statistical method based on power regression analyses was compared to the emerging computationally powerful techniques based on artificial neural network (ANN). The regression models of TCARI 、SAVI 、MSAVI and RDVIgreen were found to be more suitable for predicting Chlorophyll Density when only traditional statistical method was used especially TCARI and RDVI. ANN method was more appropriate to develop prediction models. The comparisons between these two methods were based on analysis of the statistic parameters. Results obtained using Root Mean Square Error (RMSE) for ANNs were significantly lower than the traditional method. From this analysis it is concluded that the neural network is more robust to train and estimate crop Chlorophyll Density from remote sensing data.

Research of Intelligent Substation Condition Monitoring Based on the Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 692-696
Author(s):  
Da Peng Chai ◽  
Qiang Qiang Xue ◽  
Ling Mei Wang ◽  
Xing Yong Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Electric Power ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Power Equipment ◽  
The Neural Network ◽  
Condition Monitoring System ◽  
Artificial Neural Network Ann

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).

scholarly journals Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

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