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Artificial neural network models for prediction of net radiation over a tropical region

neural COMPUTING & APPLICATIONS 2021年第12期33卷 6865-6877页

作者： Ojo, Olusola Samuel Adeyemi, Babatunde Oluleye, Daniel Oluwagbenga Fed Univ Technol Akure Dept Phys PMB 704 Akure Nigeria

In this study, net radiation and temperature data were collected from the Nigeria Meteorological Agency, Lagos, covering a period of 1983-2013. The study uses a multiple layer perceptron (MLP) model of artificial neural network (ANN) with three algorithms namely the Gradient Descent, Conjugate Gradient, and Broyden-Fletcher-Goldfarb-Shanno for the prediction of net radiation using the temperature series as input variables in place of commonly used empirical statistical methods. The investigation is conducted in a tropical region where 16 meteorological stations spatially distributed across the four climatic regions of Nigeria viz semi-arid (SAR), sub-humid dry (SHD), sub-humid humid (SHH), and humid (HUM) regions were used as a case study. Analyses showed that minimum temperature has a greater positive impact on net radiation than maximum temperature. This has been attributed among other factors to have contributed to higher magnitudes of net radiation in humid regions than those of arid regions. Meanwhile, the performance assessment of the ANN models using the refined index (d(r)) metric showed that among the three models proposed, the MLP-BFGS model performed best having maximum values of 0.91 in the SAR, 0.65 in the SHD, 0.80 in the SHH, and 0.88 in the HUM regions. It also has the lowest error analysis with magnitudes of root mean square error values of 7.92 W/m(2) in the SAR, 9.95 W/m(2) in the SHD, 12.91 W/m(2) in the SHH, and 8.73 W/m(2) in the HUM regions. Finally, it can be inferred from the results that MLP neural networks with the BFGS algorithm can predict net radiation accurately better than any empirical statistical methods over Nigeria.

关键词： Net radiation Prediction neural network algorithms Performance assessment Tropical region

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neural-network-BASED ALGORITHM FOR POWER TRANSFORMER DIFFERENTIAL RELAYS

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1995年第4期142卷 386-392页

作者： BASTARD, P MEUNIER, M REGAL, H PROTECT & CONTROL DPT F-38000 GRENOBLEFRANCE

A neural network-based algorithm for the protection of a one-phase power transformer is considered. The neural network input is a four-dimensional vector obtained by a fast-frequency analysis of the differential current. Primary and secondary currents are measured with current transformers the saturation of which is taken into account. A set of training cases is generated with the help of the electromagnetic transients program. The neural network-based algorithm is compared with a conventional differential algorithm. It is found to be more efficient, especially in the case of saturation of the current transformers.

关键词： POWER RELAYS CURRENT TRANSFORMERS PROTECTION SYSTEMS neural network algorithms

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A space-time neural network

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International Journal of Approximate Reasoning 1992年第2期6卷 133-149页

作者： Villarreal, James A. Shelton, Robert O. Information Systems Directorate Lyndon B. Johnson Space Center National Aeronautics and Space Administration Houston TX United States

neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to processing of temporal data has been severely restricted. This work introduces a novel technique that adds the dimension of time to the well-known backpropagation neural network algorithm. The paper cites several reasons why the inclusion of automated spatial and temporal associations are crucial to effective systems modeling. An overview of other works that also model spatiotemporal dynamics is furnished. In addition, a detailed description of the processes necessary to implement the space-time neural network (STNN) algorithm is provided. The reader is given several demonstrations that illustrate the capabilities and performance of this new architecture. © 1992.

关键词： backpropagation neural network algorithms space-time neural network

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Hybrid Stochastic and neural network Approach for Efficient FPGA Implementation of a Field-oriented Induction Motor Drive Controller

Hybrid Stochastic and Neural Network Approach for Efficient ...

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41st Annual Meeting of the IEEE Industry-Applications-Society (IAS)

作者： Zhang, Da Li, Hui Florida State Univ Dept ECE Tallahassee FL 32310 USA

ISBN: (纸本)9781424403646

The FPGA (Field Programmable Gate Arrays) is concurrent, executing all its logic in parallel, therefore is good for neural network applications that are characterized as heavy parallel calculation algorithms. The stochastic arithmetic can simplify the computation elements and is compatible with modern VLSI design. This paper presents an efficiency approach for a single FPGA to implement the field-oriented control of induction motor drive based on stochastic theory and neural network algorithm. A stochastic neural network structure is proposed for a feed-forward neural network to estimate the feedback signals in an induction motor drive. A new stochastic PI speed controller is developed with anti-windup function to improve the speed control performance. By applying the stochastic theory and neural network structure, the proposed algorithms enhance the arithmetic operations of the FPGA, save digital resources, simplify the algorithms, significantly reduce the cost and provide design flexibility and extra fault tolerance for the system. A hardware-in-the-loop test platform using Real Time Digital Simulator (RTDS) is built in the laboratory. The experimental results are provided to verify the proposed FPGA controller(1).

关键词： FPGA induction motor drive stochastic theory neural network algorithms

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Evaluation Model of English Teaching Effect Based on neural network Algorithm and Support Vector Machine 1

Evaluation Model of English Teaching Effect Based on Neural ...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Li, Cui Wuhan Donghu University Hubei Wuhan China

ISBN: (纸本)9798350316469

The optimize the analysis of support vector machines in response to the problem that traditional English teaching models cannot solve the accuracy and efficiency of student English teaching effectiveness evaluation, nor can they meet the requirements of English teaching effectiveness evaluation. Firstly, a multi-indicator English teaching effectiveness evaluation system with teachers and students as the main body was constructed using relevant theories, and the indicators were divided according to the data requirements of English teaching effectiveness evaluation indicators to reduce support vector machines in interference factors. Then, the neural network algorithm is used to solve the optimal solution of the kernel function parameters and regularization parameters of the support vector machine, forming a support vector machine scheme, and the support vector machine results are comprehensively analyzed. MATLAB simulation shows that neural network algorithms and support vector machines have the best evaluation accuracy and short evaluation time for English teaching effectiveness under certain evaluation criteria. © 2023 IEEE.

关键词： English language teaching evaluation model neural network algorithms Support vector machines

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Optimization method of machining parameters based on intelligent algorithm

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DISTRIBUTED AND PARALLEL DATABASES 2022年第4期40卷 737-752页

作者： Cai, Jie Zhang, Wei Deng, Jinlian Zhao, Weisheng Zhejiang Inst Mech & Elect Engn Sch Intelligent Mfg Hangzhou 310053 Zhejiang Peoples R China Zhejiang Inst Mech & Elect Engn Sch Creat Arts & Design Hangzhou 310053 Zhejiang Peoples R China Zhejiang Inst Mech & Elect Engn Sch Automat Hangzhou 310053 Zhejiang Peoples R China

The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements.

关键词： Intelligent algorithms Processing parameters Heavy-duty CNC machine tools neural network algorithms

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Biosensing with microbial fuel cells and artificial neural networks: Laboratory and field investigations

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2013年 130卷 369-374页

作者： Feng, Yinghua Harper, Willie F., Jr. Univ Pittsburgh Swanson Sch Engn Dept Civil & Environm Engn Pittsburgh PA 15261 USA US Air Force Inst Technol Dept Syst Engn & Management Wright Patterson AFB OH 45433 USA

In this study microbial fuel cell-based biosensing was integrated with artificial neural networks (ANNs) in laboratory and field testing of water samples. Inoculation revealed two types of anode-respiring bacteria (ARB) induction profiles, a relatively slow gradual profile and a faster profile that was preceded by a significant lag time. During laboratory testing, the MFCs generated well-organized normally distributed profiles but during field experiments the peaks had irregular shapes and were smaller in magnitude. Generally, the COD concentration correlated better with peak area than with peak height. The ANN predicted the COD concentration (R-2 = 0.99) with one layer of hidden neurons and for concentrations as low as 5 mg acetate-COD/L. Adding 50 mM of 2-bromoethanesulfonate amplified the electrical signals when glucose was the substrate. This report is the first to identify two types of ARB induction profiles and to demonstrate the power of ANNs for interpreting a wide variety of electrical response peaks. Published by Elsevier Ltd.

关键词： Microbial fuel cells Biosensing neural network algorithms Methanogenesis Water quality Inoculation

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neural networkS IN-PROCESS PLANT MODELING AND CONTROL

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COMPUTING & CONTROL ENGINEERING JOURNAL 1994年第3期5卷 131-134页

作者： TURNER, P MONTAGUE, GA MORRIS, AJ Dept. of Chem. & Process Eng. Newcastle upon Tyne Univ. UK

The Teaching Company Scheme offers a great deal of opportunity and benefits to both the industrial and academic partners involved. This article pays particular reference to an ongoing programme that exists between ICI... 详细信息

关键词： University of Newcastle-upon-Tyne neural network algorithms chemical engineering computing Production engineering computing neural network theory process plant control ICI process computer control ICI Engineering neural nets Teaching Company Scheme Control engineering computing chemical industry Chemical industry Chemical engineering computing process plant modelling chemical processes neural computing techniques Industrial processes Industrial applications of IT

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PARTIAL DISCHARGE PULSE DISTRIBUTION PATTERN-ANALYSIS

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IEE PROCEEDINGS-SCIENCE MEASUREMENT AND TECHNOLOGY 1995年第1期142卷 46-50页

作者： TANAKA, T KYUSHU UNIV KONAE RES LABKONAETOKYO 201JAPAN

Statistical data analysis has been recently requisite for partial discharge (PD) study. This has been enabled by the recent development of microprocessors or the downsizing of computers. It is important to pay more attention to profiles of a packet of PD pulses on the voltage phase angle. Statistical parameters such as skewness and kurtosis have been intensively investigated for PD analysis. More recently, the neural network algorithm has been utilised especially to discriminate PD signals from noise signals for insulation diagnosis.

关键词： neural network algorithms INSULATION DIAGNOSIS

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Nonlinear time series classification using bispectrum-based deep convolutional neural networks

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2020年第5期36卷 877-890页

作者： Parker, Paul A. Holan, Scott H. Ravishanker, Nalini Univ Missouri Dept Stat 146 Middlebush Hall Columbia MO 65211 USA US Bur Census 4600 Silver Hill Rd Washington DC 20233 USA Univ Connecticut Dept Stat Storrs CT 06269 USA

Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of technological advances. A motivating example is that of Google trends data, which exhibit highly nonlinear behavior. Although a rich literature exists for addressing this problem, existing approaches mostly rely on first- and second-order properties of the time series, since they typically assume linearity of the underlying process. Often, these are inadequate for effective classification of nonlinear time series data such as Google Trends data. Given these methodological deficiencies and the abundance of nonlinear time series that persist among real-world phenomena, we introduce an approach that merges higher order spectral analysis with deep convolutional neural networks for classifying time series. The effectiveness of our approach is illustrated using simulated data and two motivating industry examples that involve Google trends data and electronic device energy consumption data.

关键词： deep learning higher-order spectra neural network algorithms nonlinear time series supervised learning

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