scholarly journals Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

Minerals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 755
Author(s):  
Philipp Menesklou ◽  
Tabea Sinn ◽  
Hermann Nirschl ◽  
Marco Gleiss
Keyword(s):  
Neural Network ◽  
Process Model ◽  
Mechanistic Model ◽  
Box Model ◽  
Separation Processes ◽  
Simulation Tools ◽  
The Neural Network ◽  
Sediment Consolidation ◽  
Numerical Process ◽  
Solid Liquid

Continuously operating decanter centrifuges are often applied for solid-liquid separation in the chemical and mining industries. Simulation tools can assist in the configuration and optimisation of separation processes by, e.g., controlling the quality characteristics of the product. Increasing computation power has led to a renewed interest in hybrid models (subsequently named grey box model), which combine parametric and non-paramteric models. In this article, a grey box model for the simulation of the mechanical dewatering of a finely dispersed product in decanter centrifuges is discussed. Here, the grey box model consists of a mechanistic model (as white box model) presented in a previous research article and a neural network (as black box model). Experimentally determined data is used to train the neural network in the area of application. The mechanistic approach considers the settling behaviour, the sediment consolidation, and the sediment transport. In conclusion, the settings of the neural network and the results of the grey box model and white box model are compared and discussed. Now, the overall grey box model is able to increase the accuracy of the simulation and physical effects that are not modelled yet are integrated by training of a neural network using experimental data.

A Hybrid Modeling Technique for Partially-Known Systems Using Linear Regression and Neural Network

2009 ◽  
Author(s):  
Andrew J. Joslin ◽  
Chengying Xu
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Least Squares ◽  
Process Model ◽  
Modeling Method ◽  
Hybrid Modeling ◽  
Model Description ◽  
Least Squares Regression ◽  
The Neural Network ◽  
Macro Scale

In this paper a hybrid modeling and system identification method, combining linear least squares regression and artificial neural network techniques, is presented to model a type of dynamic systems which have an incomplete analytical model description. This approach in modeling nonlinear, partially-understood systems is particularly useful to the study of manufacturing processes, where the linear regression portion of the hybrid model is established using a known mathematical model for the process and the neural network is constructed using the residuals from the least squares regression, therefore ensuring a more precise process model for the specific machining setup, tooling selection, workpiece properties, etc. In this paper the method is mathematically proven to give regression coefficients close to those which would be found if only a regression had been performed. The modeling method is then simulated for a macro-scale hard turning process, and the result proves the effectiveness of the proposed hybrid modeling method.

BP Neural Network Model Selection of Software Reliability Based on MATLAB

2015 ◽  
Vol 727-728 ◽  
pp. 991-995
Author(s):  
Shao Yun Song ◽  
Mao Luo ◽  
Hong Ming Zhou
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Bp Neural Network ◽  
Matlab Simulation ◽  
Improved Method ◽  
Simulation Tools ◽  
The Neural Network ◽  
Bp Neural Network Model ◽  
Model Year ◽  
Selection Of

Research and analysis of the BP neural networkstructure and features. Find its shortcomingsand propose an improved method for the deficiencies, and establish the neural network softwarereliability of the new model.Through MATLAB simulation tools forexamples of simulation, confirmed the new model year with the traditional modelof high-precision, the characteristics of generalization stronger.

scholarly journals Progress in neural network based techniques for signal integrity analysis–a survey

2019 ◽  
Vol 8 (1) ◽  
pp. 276-282
Author(s):  
Chan Hong Goay ◽  
Azniza Abd Aziz ◽  
Nur Syazreen Ahmad ◽  
Patrick Goh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Integrity ◽  
Circuit Modeling ◽  
Modeling Tool ◽  
Simulation Tools ◽  
The Neural Network ◽  
Data Rates ◽  
Recent Advancement ◽  
Integrity Analysis

With the increase in data rates, signal integrity analysis has become more time and memory intensive. Simulation tools such as 3D electromagnetic field solvers can be accurate but slow, whereas faster models such as design equations and equivalent circuit models lack accuracy. Artificial neural networks (ANNs) have recently gained popularity in the RF and microwave circuit modeling community as a new modeling tool. This has in turn spurred progress towards applications of neural networks in signal integrity. A neural network can learn from a set of data generated during the design process. It can then be used as a fast and accurate modeling tool to replace conventional approaches. This paper reviews the recent advancement of neural networks in the area of signal integrity modeling. Key advancements are considered, particularly those that assist the ability of the neural network to cope with an increasing number of inputs and handle large amounts of data.

scholarly journals A Neural Networks Approach to Determine Factors Associated With Self-Reported Discomfort in Picking Tasks

2021 ◽  
pp. 001872082110476
Author(s):  
Olfa Haj Mahmoud ◽  
Charles Pontonnier ◽  
Georges Dumont ◽  
Stéphane Poli ◽  
Franck Multon
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Environmental Variables ◽  
Environmental Data ◽  
Neural Network Approach ◽  
Data Set ◽  
Simulation Tools ◽  
The Neural Network ◽  
Input Variables ◽  
Similar Accuracy

Objective A neural networks approach has been proposed to handle various inputs such as postural, anthropometric and environmental variables in order to estimate self-reported discomfort in picking tasks. An input reduction method has been proposed, reducing the input variables to the minimum data required to estimate self-reported discomfort with similar accuracy as the neural network fed with all variables. Background Previous works have attempted to explore the relationship between several factors and self-reported discomfort using observational methods. The results showed that this relationship was not a simple linear relationship. Another study used neural networks to model the function returning reported discomfort according to static posture, age, and anthropometrics variables. The results demonstrated the model’s ability to predict reported discomfort. But all the available variables were used to design the neural network. Method Eleven subjects carried-out picking tasks with various masses (0, 1, 3 kg) and imposed duration (5, 10, or 15 s). Continuous REBA score, anthropometric and environmental data were computed, and subjects’ discomfort were collected. The data set of this work consisted in the computed continuous REBA score, anthropometric, environmental data and collected subjects’ discomfort. Results The results showed that the correlation between the estimated and experimental tested data was equal to 0.775 when using all the 14 available variables. After data reduction, only 6 variables were left, with a very close performance when predicting discomfort. Conclusion A neural network approach has been proposed to estimate self-reported discomfort according to a minimum set of postural, anthropometric and environmental variables in picking tasks. Application This method has the potential to support ergonomists in workstation designing processes, by adding discomfort prediction to virtual manikins’ behaviors in simulation tools.

A neural network approach to the modelling and analysis of stereolithography processes

2001 ◽  
Vol 215 (12) ◽  
pp. 1719-1733 ◽  
Author(s):  
S H Lee ◽  
W S Park ◽  
H S Cho ◽  
W Zhang ◽  
M C Leu
Keyword(s):  
Neural Network ◽  
Process Model ◽  
Dimensional Accuracy ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Improve Accuracy ◽  
Input Parameters ◽  
Part Distortion ◽  
Parameter Ranges ◽  
The Relationship

Stereolithography has attracted more attention due to better part build accuracy than other rapid prototyping technologies. However, this build method still limits wider applications due to the unsatisfactory level of dimensional accuracy that remains with the current technology. To improve accuracy and reduce part distortion, understanding the physics involved in the relationship between the operating input parameters and the part dimensional accuracy is prerequisite. In this paper, this causality is identified through a process model obtained via an artificial neural network based upon 140 actual build parts. The network is so constructed that it relates the process input parameters to part dimensional accuracy. The neural network model is found to predict the effects of the input parameters on the accuracy with reasonable accuracy. The prediction performance is discussed in detail for various process parameter ranges.

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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