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Segmented parabolic adjustment of the FAST reflector utilizing spatial coordinate rotation transformation

MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第10期35卷 106009-106009页

作者： Wang, Fengzhou Kang, Yuchuan Guo, Fenze Jilin Univ Sch Mech & Aerosp Engn Changchun 130025 Peoples R China Sch Disaster Prevent Sci & Technol Coll Earth Sci Beijing 101601 Peoples R China

Since the middle of the twentieth century, the advent of radio telescopes has brought a whole new way and approach to astronomical observation. For Arecibo-type radio telescopes, the tuning optimization of the active reflecting surface (working paraboloid) is the main factor affecting the reflectance calibration. In this study, leveraging the transformation of spatial coordinates through rotation, we introduce an innovative optimization model specifically for the segmented paraboloid of the Five-hundred-meter Aperture Spherical radio Telescope (Hereinafter referred to as FAST) designed by China astronomer and scientist Nan Rendong. This research constructs the equation for an ideal paraboloid and adjusts the working paraboloid to fit within specified constraints such as the orientation of the target star, the adjustment limit of the actuator, and the spatial coordinates. The study employs a combination of coarse and fine grid searches to identify and record the optimal adjustment scheme of the main cable nodes at different angles and the corresponding 2226 actuator coordinates and telescoping length, based on which we build a back propagation model to continuously modify the adjustment scheme. A combination of geometric simulation and Monte Carlo tests were also used for verification. Furthermore, we delve into the impact of variations between adjacent nodes of the modulating actuators, as well as potential longitudinal and radial changes. Compared to the conventional conditioning model, the segmented solution idealized paraboloid we created increases the original reflection efficiency from 77.92% to 95.56% in the working area of 300 m aperture, it will contributes to enhancing the overall performance of FAST.

关键词： reflective surface adjustment spatial coordinate rotation transformation geometric simulation monte carlo test back propagation algorithm

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Non-Intrusive Load Monitoring Based on Unsupervised Optimization Enhanced Neural Network Deep Learning

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FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Liu, Yu Wang, Jiarui Deng, Jiewen Sheng, Wenquan Tan, Pengxiang Southeast Univ Sch Elect Engn Nanjing Peoples R China

Non-intrusive load monitoring has broad application prospects because of its low implementation cost and little interference to energy users, which has been highly expected in the industrial field recently due to the development of learning algorithms. Targeting at the investigation of practical and reliable load monitoring in field implementations, a non-intrusive load disaggregation approach based on an enhanced neural network learning algorithm is proposed in this article. The presented appliance monitoring approach establishes the neural network model following the supervised learning strategy at first and then utilizes the unsupervised learning based optimization to enhance the flexibility and adaptability for diverse scenarios, leading to the improvement of disaggregation performance. By verifications on the REDD public dataset, the proposed approach is demonstrated to be with good performance in non-intrusive load monitoring. In addition to the accuracy enhancement, the proposed approach is also with good scalability, which is efficient in recognizing the newly added appliance.

关键词： unsupervised learning NILM neural network BP back propagation algorithm electricity consumption behavior analysis k-means clustering

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Neural Network Based PI Controller Parameter Calculation on a Boiler Drum Level System

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Procedia Technology 2016年 24卷 1616-1622页

作者： Simna Surendran Vimal Kumar Mtech Scholar Department of Applied Electronics & Instrumentation Vimal Jyothi Engineering College Chemperi Kannur India Assistant Professor Department of Applied Electronics & Instrumentation Vimal Jyothi Engineering College Chemperi Kannur India

The controller parameters influence the performance of the closed loop system. So we have to develop a tuning method for obtaining the optimum values of the controller parameters with respect to a particular process. Controller tuning is very much process dependent and any improper selection of the controller settings may lead to instability and affect performance of the closed loop system. Closed loop tuning methods like Ziegler-Nichols method depends on estimation of ultimate gain and ultimate time period. When trying different gains on an unknown process the amplitude of undampened oscillations can become unsafe or on the conversely for low initial gain settings the test can take a long time to reach sustained oscillation condition. This paper proposes a neural network based scheme to estimate ultimate gain and optimum proportional and integral value of PI controller within affordable time limit and safe input range when the parameters change.

关键词： back propagation algorithm Levenberg-Marquadrt algorithm Neural network PI controller

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Neural Network Approach Based on Agent to Predict Stock Performance

International Journal of Systems and Control

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International Journal of Systems and Control 2008年第3期 173-179页

作者： SHAO Yan-hua1,2,LI Jian-shi1,CHEN Tian-jian 1,TIAN Feng1,WANG Jin-rong1 1.Department of Computer Science and Technology,University of Guizhou,Guiyang 550025,China 2.Department of Economics and Management,Guizhou University for Nationalities,Guiyang 550025,China

Predicting stock performance is a very large and profitable area of study.A large number of studies have been reported in literature with reference to the use of artificial neural network in modeling stock performance in western ***,not much work along the approach to neural network based on agent has been *** thesis focuses on the development and the simulation of a stock market performance model of utilizing a neural network approach base on *** model is easy to understand,and can be easily implemented as a software *** we will discuss the basic concepts behind this type of neural network based on agent,then,we'll get into some of the more application ideas.

关键词： Artificial Neural Network back propagation algorithm Agent Swarm Simulation Toolkit

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NEURO-FUZZY ADAPTIVE CONTROL AND MODELING A THRUSTER OF AUTONOMOUS NDERWATER VEHICLES

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IFAC Proceedings Volumes 2006年第16期39卷 843-849页

作者： F. Palis Y. Tsepkovskiy V. Filaretov D. Ukhimets Magdeburg University Institute of Electric Energy Systems Universitätplatz2 / Postfach 4120 39106 Magdeburg Deutschland Tel:(+49)-391-67-18135. Fax:(+49)-391-67-12481 Robotic laboratory Institute for automation and control process Nekrasovskaya str 76 105 Vladivostok 690014 the Russian Federation Tel: + 7 4232 26694 +7 ***

The paper presents a direct adaptive fuzzy approach for parameter identification and control of unknown nonlinear systems. To prove the performance of the proposed method an autonomous underwater vehicle (AUV) is modeled using an Adaptive Neuro-Fuzzy Inference System (ANFIS). To guaranty high accuracy the model is adapted online using system input and output data. From the model the current process parameters are identified and utilized for controller design. Here, Single Step Ahead Direct Adaptive Control and Multi Steps Ahead Direct Control schemes are considered.

关键词： underwater vehicles adaptive filters back propagation algorithm predictive control fuzzy modelling

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Identification of a steam generator using focused time lagged recurrent neural network model with gamma memory

Journal of the Institution of Engineers (India), Part CP: Co...

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Journal of the Institution of Engineers (India), Part CP: Computer Engineering Division 2007年第NOV.期88卷 32-36页

作者： Dudul, S.V. Department of Applied Electronics Amravati University Amravati 444602 India

This paper investigates the identification of a typical steam generator, which shows very rich non-linear dynamics using a focused time lagged recurrent (FTLR) neural network (NN). It appears from the literature review that an optimal NN model for the identification of such a highly non-linear complex dynamical system is not currently available. This paper compares the performance of two NN configurations, namely a well known MLP NN and the proposed FTLRNN. A standard static back propagation algorithm with momentum term has been used for both the models. It is shown that the estimated dynamic NN model comprising of a gamma memory (GM) filter followed by a MLP NNclearly outperforms the static MLP NNin NMSE, correlation coefficients and regression characteristics on the cross-validation (CV)as well as testing datasets. In addition, the outputs of the proposed FTLRNN model reasonably follow the desired outputs of the steam generator system for the testing and CVexemplars. This also means that most of the information about the rich non -linear dynamics of the system has been extracted satisfactorily from the training dataset and that the proposed model approximates the given system with pragmatic accuracy. It has been shown that the proposed FTLRNN model has a remarkable system identification capability for the problem considered in this paper.

关键词： back propagation algorithm Focused time lagged recurrent NN Gamma memory Multi-layer perceptron NN Non-linear system System identification

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Control of DSTATCOM Using ANN-BP algorithm for the Grid Connected Wind Energy System

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ENERGIES 2022年第19期15卷 6988-6988页

作者： Irfan, Mohammad Mujahid Malaji, Sushama Patsa, Chandrashekhar Rangarajan, Shriram S. Hussain, S. M. Suhail Jawaharlal Nehru Technol Univ Dept Elect & Elect Engn Hyderabad 500085 Telangana India SR Univ Dept Elect & Elect Engn Warangal 506371 Telangana India Mahatma Gandhi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 Telangana India Clemson Univ Dept Elect & Comp Engn Clemson SC 29634 USA King Fahd Univ Petr & Minerals KFUPM Elect Engn Dept Dhahran 31261 Saudi Arabia

Green energy sources are implemented for the generation of power due to their substantial advantages. Wind generation is the best among renewable options for power generation. Generally, the wind system is directly connected with the power network for supplying power. In direct connection, there is an issue of managing power quality (PQ) concerns such as voltage sag, swells, flickers, harmonics, etc. In order to enhance the PQ in a power network with a wind energy conversion system (WECS), peripheral compensation is needed. In this paper, we highlight a novel control technique to improve the PQ in WECS by adopting an Artificial Neural Network (ANN)-based Distribution Static Compensator (DSTATCOM). In our proposed approach, an online learning-based ANN back propagation (BP) model is used to generate the gate pulses of the DSTATCOM, which mitigate the harmonics at the grid side. It is modelled using the MATLAB platform and the total harmonic distortion (THD) of the system is compared with and without DSTATCOM. The harmonics at the source side decreased to less than 5% and are within the IEEE limits. The results obtained reveal that the proposed online learning-based ANN-BP is superior in nature.

关键词： wind energy conversion system ANN back propagation algorithm power quality total harmonic distortion

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Neural network design with combined backpropagation and creeping-random-search learning algorithms applied to the determination of retained austenite in TRIP steels

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REVISTA DE METALURGIA 2010年第6期46卷 499-510页

作者： Toda-Caraballo, I. Garcia-Mateo, C. Capdevila, C. CSIC Ctr Nacl Invest Met CENIM Dep Met Fis Grp Materalia Madrid 28040 Spain

At the beginning of the decade of the nineties, the industrial interest for TRIP steels leads to a significant increase of the investigation and application in this field. In this work, the flexibility of neural networks for the modelling of complex properties is used to tackle the problem of determining the retained austenite content in TRIP-steel. Applying a combination of two learning algorithms (backpropagation and creeping-random-search) for the neural network, a model has been created that enables the prediction of retained austenite in low-Si / low-Al multiphase steels as a function of processing parameters.

关键词： Neural Networks Creeping-random-search back propagation algorithm Retained austenite TRIP

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Investigation of Artificial Intelligence Techniques in Finance and Marketing

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Procedia Computer Science 2020年 173卷 149-157页

作者： Raman Tiwari Smriti Srivastava Rajat Gera Faculty of Management Studies Manav Rachna University Faridabad - 121004 India Division of Instrumentation and Control Netaji Subhas Institute of Technology New Delhi-110078 India Faculty of Management Studies Manav Rachna International University Faridabad - 121004 India

The paper presents the application of artificial intelligence techniques-artificial neural network (ANN) and fuzzy logic to the problems of stock market prediction, sales forecasting and market segmentation. back propagation algorithm has been used to train the neural network for the first two problems, while Self-Organizing Maps (SOM) have been employed for the clustering problem that Market Segmentation presents. The Fuzzy model discussed in the paper uses the state values or the input-output data pairs. Mean Square Error has been used as the performance index to compare ANN and Fuzzy logic for stock prediction and sales forecasting.

关键词： Artificial neural network Fuzzy logic Stock market prediction Sales forecasting Market segmentation back propagation algorithm Self-organizing maps

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Soft Sensor for Capacitance Tracking in Quality Assurance of Fruit Ripening Process

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Materials Today: Proceedings 2018年第10期5卷 20785-20792页

作者： K. Sujatha V. Srividhya R.S. Ponmagal T. Godhavari Dept. of EEE/CSE/ECE Center for Electronics Automation & Industrial Research (CEAIR) Dr.MGR Educational & Research Institute Chennai India Dept. of EEE Center for Electronics Automation & Industrial Research (CEAIR) Dr.MGR Educational & Research Institute Chennai India

The highlight of this research work is to discover an efficient capacitance tracking for generation of ethylene gas (C 2 H 4 in ppm) used for ripening of fruits by employing soft sensor built using image processing and Artificial Neural Networks (ANN) algorithms. The proposed method relies on the statistical analysis of the color which denotes the various stages in ripening and in turn indicates the amount of ethylene gas required. The changes in color, texture, intensity variation, mean, variance and standard deviation extracted from the images are the features which enable the personnel to determine the amount of ethylene gas. The Feed Forward Neural Network (FFNN) is used for ethylene gas and the corresponding capacitance value estimation. This is made possible using back propagation algorithm (BPA) for training the FFNN. As a part of image processing the intensity values in color images and its variation are tracked by dithering which is used as a unique feature input to train the FFNN. The major findings of the proposed method depends on the FFNN estimating the ethylene gas needed for ripening process in a feed forward fashion thereby providing the precision and recall values spontaneously for every instance. The improvement made in application side denotes that earlier a circuit with capacitance is used to generate ethylene gas for this purpose which is on other hand replaced by using a soft sensor. Nearly 51 images of banana are considered for training and testing respectively. Testing and confirmation result shows the required precision and recall values are in range of 80 to 89% and 100% respectively .

关键词： Feature Extraction Dithering Fruit Ripening back propagation algorithm

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