scholarly journals INTELLIGENT LIQUID LEVEL CONTROL OF A COUPLED NONLINEAR THREE TANK SYSTEM SUBJECTED TO VARIABLE FLOW PARAMETERS

2021 ◽  
Vol 27 (1) ◽  
pp. 55-63
Author(s):  
MUSTEFA JIBRIL ◽  
MESAY TADESSE ◽  
NURYE HASSEN
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Moving Average ◽  
Level Control ◽  
Fractional Flow ◽  
Variable Flow ◽  
Intelligent Control System ◽  
Point Tracking ◽  
Algorithm Comparison ◽  
Tank System

In this paper, an intelligent control system technique is proposed to model and control of a nonlinear coupled three tank system. Two pumps fed the tank 1 and tank 2 and a fractional flow of these two pumps fed tank 3. The main aim of this paper is to make a set point tracking experiments of the tanks level using a nonlinear autoregressive moving average L-2 (NARMA L-2) and neural network predictive controllers. The proposed controllers are designed with the same neural network architecture and algorithm. Comparison of the system with the proposed controllers for tracking a step and random level set points for a fixed and variable flow parameter and some good results have been obtained.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

Level Control of Coupled Tank System Based on Neural Network Techniques

2019 ◽  
Vol 15 (3) ◽  
Author(s):  
B. S. Sousa ◽  
F. V. Silva ◽  
A. M. F. Fileti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mimo System ◽  
Level Control ◽  
Small Plant ◽  
Control Techniques ◽  
Advanced Control ◽  
Tank System ◽  
Universal Approximators ◽  
Linear Controllers

AbstractThe control design of coupled tanks is not an easy task due to the nonlinear characteristic of the valves, and the interactions between the controlled variables. Those features pose a challenge in the automatic control, so that linear controllers, such as conventional PID, might not work properly for regulating this MIMO system. Some advanced control techniques (e. g. control based on neural networks) can be used since neural networks are universal approximators which can deal with nonlinearities and interactions between process variables. In the present work, an experimental investigation was performed presenting a comparison between two neural network-based techniques and testing the feasibility of these techniques in the coupled tanks system. First principles simulations helped to find suitable parameters for the controllers. The results showed that the model predictive control based on artificial neural networks presented the best performance for supervisory tests. On the other hand, the inverse neural network needed a very accurate model and small plant-model mismatches led to undesirable offsets.

scholarly journals Neural Networks in Forecasting Disease Dynamics

2020 ◽  
Vol 10 (3) ◽  
pp. 198-204
Author(s):  
A. G. Hasanov ◽  
D. G. Shaybakov ◽  
S. V. Zhernakov ◽  
A. M. Men’shikov ◽  
F. F. Badretdinova ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Comparative Analysis ◽  
Network Architecture ◽  
Time Window ◽  
Moving Average ◽  
Fixed Time ◽  
Gradient Algorithms ◽  
Medical Histories ◽  
Hidden Layer

Introduction.In recent years, computer technologies are more and more widely used in medicine. Thus, medical neuro‑ informatics solves diagnostic and forecasting tasks using neural networks.Materials and methods. Using the example of erysipelas, the possibility of forecasting the course and outcome of the dis‑ ease is demonstrated. A retrospective study of the medical histories of patients treated for erysipelas at the Ufa Clinical Hospital No.8 during 2006–2015 was carried out. Modern statistical packages and the MATLAB environment were used.Results and discussion.The conducted comparative analysis showed a 3-layer recurring network of direct distribution to be the most suitable neural network architecture. The optimal structure of the neural network was found to be: 27–6–1 (i.e. 27 neurons are used at the entrance; 6 — in a hidden layer; 1 — in the output layer). The best convergence of the network learning process is provided by the quasi-Newton and conjugated gradient algorithms. In order to assess the effectiveness of the proposed neural network in predicting the dynamics of inflammation, a comparative analysis was carried out using a number of conventional methods, such as exponential smoothing, moving average, least squares and group data handling.Conclusion.The proposed neural network based on approximation and extrapolation of variations in the patient’s medi‑ cal history over fixed time window segments (within the ‘sliding time window’) can be successfully used for forecasting the development and outcome of erysipelas. 

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.

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

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