The health development is one of the most important challenges in the world today. All human beings are affected by many diseases due to various circumstances like pollution, climate change, living habits, etc. Theref...
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The health development is one of the most important challenges in the world today. All human beings are affected by many diseases due to various circumstances like pollution, climate change, living habits, etc. Therefore, the improvement of predicting diseases is a very essential process in medical management. Prediction refers to the results of an algorithm after it has been trained on a dataset. It is a mathematical process that seeks to predict future outcomes by analyzing methods. Classification methods of machine learning can be used to find accurate prediction of disease by the symptoms. This paper reviews the gradient descent algorithm such as Logistic Regression and Artificial Neural Network. These models are highly applicable and deliver reliable prediction accuracy with the help of a dataset. The survey indicates that the most popular classification techniques are Artificial Neural Network and Logistic Regression. The major purpose of this study is to investigate the performance of various scaling methods, including ensemble normalization and standardization methods, for improving disease prediction. The study also presents a performance comparison of classification algorithms, with and without applying the feature scaling of the data preprocessing techniques. In the proposed system, two algorithms, Artificial Neural Network and Logistic Regression, were used for the classification. Firstly, the accuracy of Artificial Neural Network and Logistic Regression without scaling method was calculated. The results show that Artificial Neural Network produces the highest accuracy of 86.13% in predicting heart disease. Next, various scaling methods were applied with Artificial Neural Network and Logistic Regression algorithms to improve the accuracy of the prediction process. The experimental results show that the accuracy of Artificial Neural Network using Ensemble Normalization and Standardization is 98.81%, which is greater than the other accuracies. Finally, a s
This paper investigates the position tracking control problem for an induction motor with completely unknown nonlinearities. A novel control scheme is presented by using the gradient descent algorithm, adaptive backst...
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This paper investigates the position tracking control problem for an induction motor with completely unknown nonlinearities. A novel control scheme is presented by using the gradient descent algorithm, adaptive backstepping technique, neural networks (NNs), and extended differentiators. Differing from some existing results which only designed the adaption of weights of NNs, our proposed control strategy provides training for all the parameters of NNs, including the basis functions' widths and centers. With the help of the gradient descent algorithm and Lyapunov stability criterion, the convergence of both the NN approximation error and the system tracking error can be guaranteed. Finally, a simulation example shows the advantages of our proposed method compared with direct adaptive NN control strategy.
The paper proposes an initial rotor position detection strategy for permanent magnet synchronous motors (PMSMs) based on high-frequency elliptical voltage injection. By analyzing the response current model of PMSM, th...
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ISBN:
(纸本)9798350363272;9798350363265
The paper proposes an initial rotor position detection strategy for permanent magnet synchronous motors (PMSMs) based on high-frequency elliptical voltage injection. By analyzing the response current model of PMSM, the strategy utilizes the improved gradient descent algorithm to update and optimize the skew angle of the elliptical voltage, thereby analyzing the variation characteristics of the response current and obtaining the initial position of the rotor from it. Compared with traditional methods of initial rotor position detection, this strategy has a simpler structure, does not require specific motor parameters, and no longer relies on the cooperation of multiple filters to obtain positional information from the response current. It avoids the impact of filters on the current amplitude and reduces phase shifts caused by signal processing. The proposed initial rotor position detection strategy has been validated through simulation.
Spiking Neural Network (SNN) is the third generation of neural networks, which transmits information through the impulse train, but the discrete impulse train has the property of non-differentiable, so it is difficult...
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ISBN:
(纸本)9798350334722
Spiking Neural Network (SNN) is the third generation of neural networks, which transmits information through the impulse train, but the discrete impulse train has the property of non-differentiable, so it is difficult to apply the gradient descent algorithm to the spiking neural network. In this paper, the approximate derivative of the impulse activity is introduced to simulate the impulse activity, and then the spiking neural network based on the gradient descent algorithm is realized. On this basis, the influence of different approximate derivatives on the training accuracy of the spiking neural network is explored, and the iterative formula of LIF (Leaky Integrate and Fired) neurons is optimized and simplified. The results show that when the approximate derivative is introduced, our neural network has lower consumption, better performance, and the accuracy of the moment function model neural network is higher. We take the MNIST data set as the input of the spiking neural network, convert it into the impulse sequence information by the frequency coding method based on the impulse counting, and transmit it through the simplified LIF neuron model. On the basis of the error back propagation rules, the synaptic weight and error deviation of the neural network are constantly updated. The results show that the proposed algorithm is of higher accuracy and faster speed.
In the last few years, Motor Current Signature Analysis (MCSA) has proven to be an effective method for electrical machines condition monitoring. Indeed, compared to vibration and temperature analysis, current measure...
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In the last few years, Motor Current Signature Analysis (MCSA) has proven to be an effective method for electrical machines condition monitoring. Indeed, compared to vibration and temperature analysis, current measurement proves to be a convenient and non-invasive alternative. Moreover, it has proven to be a reliable method since many mechanical and electrical faults manifest as side-band spectral components generated around the fundamental frequency component of the motor's current. These components are called interharmonics and they are a major focus of fault detection using MCSA. However, the main drawback of this approach is that the interference of other more prevalent components such as the fundamental and noise components can obstruct the effect of interharmonics in the spectrum and may therefore affect fault detection accuracy. Thus, we propose in this paper an alternative approach that aims to decompose the different current components using a model based on a Vandermonde matrix, in order to monitor each component independently. Then, the tracking of each distinct component in time and spectral domains is implemented. This is achieved by estimating their respective relevant parameters using the gradient descent algorithm. This method has been favorably compared to an existing estimation algorithm (MUSIC) and its efficiency has been validated. The results of this work prove to be promising and establish the parametric tracking of the electrical current components using the gradient descent algorithm as a reliable monitoring approach.
Scale deposition is one of the serious oilfield chemical issues which may lead to a range of downhole and production problems, including the reduction of well productivity index. Scale Inhibitor (SI) squeeze treatment...
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Scale deposition is one of the serious oilfield chemical issues which may lead to a range of downhole and production problems, including the reduction of well productivity index. Scale Inhibitor (SI) squeeze treatments are one of the most common techniques which are applied to prevent downhole scaling in production wells. A treatment typically consists of four stages, (i) a preflush, to condition the rock surface;(ii) a main treatment, where a batch of high concentration inhibitor is bullheaded into the formation;(iii) an overflush, to displace the scale inhibitor slug deeper into the near-well formation, and (iv) a shut-in stage to allow further inhibitor retention before putting the well back on production. During this backflow period, scale inhibitor is released from the rock surface into the produced water, and the scale deposition is prevented if the inhibitor concentration is above some specified Minimum Inhibitor Concentration (MIC). Due to logistic constraints in offshore wells, it is often required that the scale protection afforded by a squeeze treatment should last for some fixed design lifetime until the next treatment becomes available. For example, in the North Sea sector, well operations are often based on an annual treatment design. This paper presents a methodology to optimize the squeeze treatment design for a fixed target lifetime and this is applied for two offshore well squeeze treatments. This approach allows us to account for the operational constraints in the squeeze optimization process to treat a fixed volume of produced water, while minimizing the inhibitor neat volume and the total pumping time. A sensitivity study was performed on the inhibitor concentration, where the results showed that deploying a smaller inhibitor slug but with higher concentration is more effective than a larger slug with lower concentration, assuming a fixed volume of inhibitor and injected water. Therefore, it is recommended that the inhibitor is deployed at the m
This paper presents a new optimal controller using the Binary gradientdescent (BGD) algorithm to manage distributed generations effectively in a grid network. The algorithm aims to minimize power consumption from the...
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In this paper,the parameter estimation for Hammerstein-Wiener nonlinear systems with unknown delay is *** on the hierarchical identification principle and two-step identification,the maximum likelihood recursive algor...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In this paper,the parameter estimation for Hammerstein-Wiener nonlinear systems with unknown delay is *** on the hierarchical identification principle and two-step identification,the maximum likelihood recursive algorithm is used to identify the parameters of the system,and the gradientdescent method is used to identify the time ***,the algorithm is verified by a numerical example,and the simulation results show that the algorithm has the characteristics of fast convergence speed and high identification accuracy.
When using multiple microwave source arrays to heat materials, coordinating the feed power of each microwave source to improve the heating efficiency and temperature uniformity has been the focus of related research. ...
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When using multiple microwave source arrays to heat materials, coordinating the feed power of each microwave source to improve the heating efficiency and temperature uniformity has been the focus of related research. This paper presents a coordinated control strategy based on gradientdescent for variable power heating in a multiple microwave source game. First, the game consensus strategy is used to coordinate the input power of each microwave source, and the reflection coefficient S11\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left| S_{11}\right|$$\end{document} is introduced to construct the game cost function. Second, to improve the heating uniformity, the gradient descent algorithm is used to obtain the heating time of each microwave source, to optimize the distribution of the electric field. Finally, the finite element method is used to construct a numerical calculation model, and verify the effectiveness of the coordinated control strategy. The numerical simulation results show that the proposed game variable power coordinated control strategy based on the gradientdescent method can further improve the temperature distribution uniformity on the basis of improving the heating efficiency. With a heating efficiency increase of 10.7%, uniformity increased by 41.5-73.8%. The proposed coordinated control strategy can improve the heating efficiency and uniformity of the temperature distribution.
In this paper, a modified gradient descent algorithm based MTPA (Maximum Torque Per Ampere) control scheme to improve the driving efficiency of a permanent magnet synchronous motor is proposed. The modified gradient d...
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ISBN:
(纸本)9788986510218
In this paper, a modified gradient descent algorithm based MTPA (Maximum Torque Per Ampere) control scheme to improve the driving efficiency of a permanent magnet synchronous motor is proposed. The modified gradient descent algorithm tracks the optimal current angle for MTPA control by using a newly defined gradient based on a change rate of copper losses. Since a magnitude of stator current is only required to track the optimal current angle for MTPA control, the proposed method is not dependent on motor parameters such as stator inductances and permanent magnet flux linkage. By using the proposed method, it is possible to track the optimal current angle that minimizes the copper losses for all load conditions in a short time, even with the motor parameters error or an offset in measured rotor position. The validity of the proposed method is verified by simulations using Matlab/Simulink for 210kW and 1.5kW IPMSMs (Interior Permanent Magnet Synchronous Motors) and experiments with a 1.5kW IPMSM.
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