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Готові списки джерел за темами / Seasonal Artificial Neural Network

Добірка наукової літератури з теми "Seasonal Artificial Neural Network"

Автор: Grafiati

Опубліковано: 19 червня 2021

Оновлено: 1 лютого 2022

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Статті в журналах з теми "Seasonal Artificial Neural Network":

1

Fallah-Gha, G. A., M. Mousavi-Ba, and M. Habibi-Nok. "Seasonal Rainfall Forecasting Using Artificial Neural Network." Journal of Applied Sciences 9, no. 6 (March 1, 2009): 1098–105. http://dx.doi.org/10.3923/jas.2009.1098.1105.

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2

Tran Anh, Duong, Thanh Duc Dang, and Song Pham Van. "Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks." J 2, no. 1 (February 14, 2019): 65–83. http://dx.doi.org/10.3390/j2010006.

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Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall.

3

Mzyece, Lillian, Mayumbo Nyirenda, Monde K. Kabemba, and Grey Chibawe. "Forecasting Seasonal Rainfall in Zambia – An Artificial Neural Network Approach." Zambia ICT Journal 2, no. 1 (June 29, 2018): 16–24. http://dx.doi.org/10.33260/zictjournal.v2i1.46.

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Weather forecasting is an ever-challenging area of investigation for scientists. It is the application of science and technology in order to predict the state of the atmosphere for a given time and location. Rainfall is one of the weather parameters whose accurate forecasting has significant implications for agriculture and water resource management. In Zambia, agriculture plays a key role in terms of employment and food security. Rainfall forecasting is one of the most complicated and demanding operational responsibilities carried out by meteorological services all over the world. Long-term rainfall prediction is even more a challenging task. It is mainly done by experts who have gained sufficient experience in the use of appropriate forecasting techniques like modelling. It is mainly done by experts who have gained sufficient experience in the use of appropriate forecasting techniques like modelling. In this paper, a rainfall forecasting model using Artificial Neural Network is proposed as a model that that can be 'trained' to mimic the knowledge of rainfall forecasting experts. This makes it possible for researchers to adapt different techniques for different stages in the forecasting process. We begin by noting the five main stages in the seasonal rainfall forecasting process. We then apply artificial neural networks at each step. Initial results show that the artificial neural networks can successfully replace the currently used processes together with the expert knowledge. We further propose the use of these neural networks for teaching such forecasting processes, as they make documentation of the forecasting process easier and hence making the educational process of teaching to forecast seasonal rainfall easier as well. Artificial Neural Networks are reliable, handle more data at one time by virtual of being computer based, are less tedious and less dependent on user experience.

4

Baldigara, Tea. "Modeliranje i prognoziranje broja zaposlenih u turizmu i hotelskoj industriji u Republici Hrvatskoj primjenom modela umjetnih neuronskih mreža." Oeconomica Jadertina 10, no. 2 (December 17, 2020): 3–20. http://dx.doi.org/10.15291/oec.3162.

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The paper investigates the performance and prognostic power of artificial neural network models in modelling and forecasting of time series of seasonal character. Models of artificial neural networks have been applied in modelling and forecasting the monthly total number of employees, the number of employed men and the number of employed women in the activity of providing accommodation services and preparing and serving food and beverages in the Republic of Croatia. The obtained modelling results have been compared with the results obtained by applying some of the traditionally used quantitative models in the analysis of seasonal time series, such as the Holt-Winters model of triple exponential smoothing and the seasonal multiplicative model of exponential trend. The evaluation of the performance and prognostic power of individual models was performed by comparing the average absolute and average absolute percentage error and the correlation coefficient between the actual and estimated values, and the predicted values were compared with the actual values. The evaluation of the obtained results showed that the selected model of acyclic multilayer perceptron is suitable for modelling and forecasting time series of seasonal character. The comparison of prognostic powers and actual and projected values of the number of employees suggests that the designed model of the artificial neural network is very reliable. This indicates that the models of artificial neural networks have great application potentials in the domain of modelling and forecasting of time series of a seasonal character.

5

S, Benkachcha, Benhra J, and El Hassani. H. "Seasonal Time Series Forecasting Models based on Artificial Neural Network." International Journal of Computer Applications 116, no. 20 (April 22, 2015): 9–14. http://dx.doi.org/10.5120/20451-2805.

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6

Oscar CLAVERIA, Oscar CLAVERIA, Enric MONTE, and Salvador TORRA. "DATA PRE-PROCESSING FOR NEURAL NETWORK-BASED FORECASTING: DOES IT REALLY MATTER?" Technological and Economic Development of Economy 23, no. 5 (November 4, 2015): 709–25. http://dx.doi.org/10.3846/20294913.2015.1070772.

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This study aims to analyze the effects of data pre-processing on the forecasting performance of neural network models. We use three different Artificial Neural Networks techniques to predict tourist demand: multi-layer perceptron, radial basis function and the Elman neural networks. The structure of the networks is based on a multiple-input multiple-output (MIMO) approach. We use official statistical data of inbound international tourism demand to Catalonia (Spain) and compare the forecasting accuracy of four processing methods for the input vector of the networks: levels, growth rates, seasonally adjusted levels and seasonally adjusted growth rates. When comparing the forecasting accuracy of the different inputs for each visitor market and for different forecasting horizons, we obtain significantly better forecasts with levels than with growth rates. We also find that seasonally adjusted series significantly improve the forecasting performance of the networks, which hints at the significance of deseasonalizing the time series when using neural networks with forecasting purposes. These results reveal that, when using seasonal data, neural networks performance can be significantly improved by working directly with seasonally adjusted levels.

7

Rezaei, Mohsen, Ahmad Ali Akbari Motlaq, Ali Rezvani Mahmouei, and Seyed Hojjatollah Mousavi. "River Flow Forecasting using artificial neural network (Shoor Ghaen)." Ciência e Natura 37 (December 21, 2015): 207. http://dx.doi.org/10.5902/2179460x20849.

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In our country, most of the rivers located in dry and warm climate areas are seasonal, and many of them have experienced floods. That, along with concerns about scarcity of water resources and the need to control surface water, makes identification, modeling, and simulation of rivers’ behavior, necessary for to long-term planning and proper and rational use of river flows potential. Rainfall phenomenon and the resulting runoff in watersheds, as well as predicting them are of nonlinear system types. Artificial neural networks are able to analyze and simulate phenomena in nonlinear and uncertain system where the relationship between the components and system parameters are not well known or describable. Shoor Ghayen River, with 100 km length is the biggest seasonal river of Qaenat city and the main source of water in Farrokhi storage dam. Therefore, in this study according to the rainfall and runoff statistic of Khonik Olya hydrometric and Ghayen synoptic stations between 1976-1977 and 2010-2011 water years, precipitation phenomena and river runoff was predicted. MATLAB software is used to perform calculations. For modeling artificial neural network, 85 percent of data were used for training the proposed method, the remaining 15% were used for validating the method using 10 neurons, and a network with an error of less than 5% was developed for each month. The maximum correlation in evaluation phase was for April with the value of 0.99, and the minimum was for June and August with a value of 0.92. Overall results indicate optimum performance of artificial neural networks in predicting runoff caused by rainfall. It is also found that better results can be achieved by standardizing the data.

8

Jayadianti, Herlina, Tedy Agung Cahyadi, Nur Ali Amri, and Muhammad Fathurrahman Pitayandanu. "METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW." Jurnal Tekno Insentif 14, no. 2 (August 27, 2020): 48–53. http://dx.doi.org/10.36787/jti.v14i2.150.

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Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR-Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), dan Artificial Neural Network-Fuzzy (ANN-Fuzzy). Hasil dari review menyimpulkan bahwa model Artificial Neural Network memiliki beberapa kelebihan dibandingkan dengan metode yang lain, yakni ANN mampu memberikan hasil yang dapat mengenali pola-pola dengan baik dan mudah dikembangkan menjadi bermacam-macam variasi sesuai dengan permasalahan maupun parameter yang ada, sehingga ANN direkomendasikan untuk perhitungan prediksi hujan. Abstract - Various kinds of research have been carried out to find accurate models to predict rainfall in various fields, so the research that has been done previously was reviewed again to help the drainage process in mining companies. The review is done by comparing the results of each model that has been conducted in several previous studies. This research used quantitative methods. Models compared in this study include the Fuzzy model, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR -Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network-Fuzzy (ANN-Fuzzy). The results of the review concluded that the Artificial Neural Network model has several advantages compared to other methods, namely ANN is able to provide results that can recognize patterns well and easily developed into a variety of variations in accordance with existing problems and parameters, so ANN is recommended for rain prediction calculation.

9

Gregorić, Maja, and Tea Baldigara. "Artificial neural networks in modelling seasonal tourism demand - case study of Croatia." Zbornik Veleučilišta u Rijeci 8, no. 1 (2020): 19–39. http://dx.doi.org/10.31784/zvr.8.1.2.

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The purpose of this paper is to design an artificial neural network in the attempt to define the data generating process of the number of German tourist arrivals in Croatia considering the strong seasonal character of empirical data. The presence of seasonal unit roots in tourism demand determinants is analysed using the approach developed by Hylleberg, Engle, Granger and Yoo – Hegy test. The study is based on seasonality analysis and Artificial Neural Networks approach in building a model which intend to describe the behaviour of the German tourist flows to Croatia. Different neural network architectures were trained and tested, and after the modelling phase, the forecasting accuracy and model performances were analysed. Model performance and forecasting accuracy evaluation was tested using the mean absolute percentage error. Based on the augmented HEGY test procedure it can be concluded the German tourist arrivals to the Republic of Croatia have nonstationary behaviour associated with the zero frequency and seasonal frequency. Taking this into consideration, in the analysis of the phenomenon it is necessary to consider its seasonal character. Given the importance of the tourism for Croatian economic development, the research results could be useful, for both, researchers and practitioners, in the process of planning and routing the future Croatian hotel industry development and improvement of business performances.

10

Sulandari, Winita, Subanar Subanar, Suhartono Suhartono, and Herni Utami. "Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model." International Journal of Advances in Intelligent Informatics 2, no. 3 (November 30, 2016): 131. http://dx.doi.org/10.26555/ijain.v2i3.69.

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Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast

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Дисертації з теми "Seasonal Artificial Neural Network":

1

Widing, Härje. "Business analytics tools for data collection and analysis of COVID-19." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176514.

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The pandemic that struck the entire world 2020 caused by the SARS-CoV-2 (COVID-19) virus, will have an enormous interest for statistical and economical analytics for a long time. While the pandemic of 2020 is not the first that struck the entire world, it is the first pandemic in history where the data were gathered to this extent. Most countries have collected and shared its numbers of cases, tests and deaths related to the COVID-19 virus using different storage methods and different data types. Gaining quality data from the COVID-19 pandemic is a problem most countries had during the pandemic, since it is constantly changing not only for the current situation but also because past values have been altered when additional information has surfaced. The importance of having the latest data available for government officials to make an informed decision, leads to the usage of Business Intelligence tools and techniques for data gathering and aggregation being one way of solving the problem. One of the mostly used software to perform Business Intelligence is the Microsoft develop Power BI, designed to be a powerful visualizing and analysing tool, that could gather all data related to the COVID-19 pandemic into one application. The pandemic caused not only millions of deaths, but it also caused one of the largest drops on the stock market since the Great Recession of 2007. To determine if the deaths or other reasons directly caused the drop, the study modelled the volatility from index funds using Generalized Autoregressive Conditional Heteroscedasticity. One question often asked when talking of the COVID-19 virus, is how deadly the virus is. Analysing the effect the pandemic had on the mortality rate is one way of determining how the pandemic not only affected the mortality rate but also how deadly the virus is. The analysis of the mortality rate was preformed using Seasonal Artificial Neural Network. Forecasting deaths from the pandemic using the Seasonal Artificial Neural Network on the COVID-19 daily deaths data.

2

BRUCE, WILLIAM, and OTTER EDVIN VON. "Artificial Neural Network Autonomous Vehicle : Artificial Neural Network controlled vehicle." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191192.

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This thesis aims to explain how a Artificial Neural Network algorithm could be used as means of control for a Autonomous Vehicle. It describes the theory behind the neural network and Autonomous Vehicles, and how a prototype with a camera as its only input can be designed to test and evaluate the algorithms capabilites, and also drive using it. The thesis will show that the Artificial Neural Network can, with a image resolution of 100 × 100 and a training set with 900 images, makes decisions with a 0.78 confidence level.
Denna rapport har som mal att beskriva hur en Artificiellt Neuronnatverk al- goritm kan anvandas for att kontrollera en bil. Det beskriver teorin bakom neu- ronnatverk och autonoma farkoster samt hur en prototyp, som endast anvander en kamera som indata, kan designas for att testa och utvardera algoritmens formagor. Rapporten kommer visa att ett neuronnatverk kan, med bildupplos- ningen 100 × 100 och traningsdata innehallande 900 bilder, ta beslut med en 0.78 sakerhet.

3

Leija, Carlos Ivan. "An artificial neural network with reconfigurable interconnection network." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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4

Alkharobi, Talal M. "Secret sharing using artificial neural network." Diss., Texas A&M University, 2004. http://hdl.handle.net/1969.1/1223.

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Secret sharing is a fundamental notion for secure cryptographic design. In a secret sharing scheme, a set of participants shares a secret among them such that only pre-specified subsets of these shares can get together to recover the secret. This dissertation introduces a neural network approach to solve the problem of secret sharing for any given access structure. Other approaches have been used to solve this problem. However, the yet known approaches result in exponential increase in the amount of data that every participant need to keep. This amount is measured by the secret sharing scheme information rate. This work is intended to solve the problem with better information rate.

5

Zhao, Lichen. "Random pulse artificial neural network architecture." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0006/MQ36758.pdf.

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6

Ng, Justin. "Artificial Neural Network-Based Robotic Control." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1846.

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Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.

7

Khazanova, Yekaterina. "Experiments with Neural Network Libraries." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1527607591612278.

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8

Brunger, Clifford A. "Artificial neural network modeling of damaged aircraft." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA283227.

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9

Tang, Chuan Zhang. "Artificial neural network models for digital implementation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq30298.pdf.

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10

Tupas, Ronald-Ray Tiñana. "Artificial neural network modelling of filtration performance." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0011/MQ59890.pdf.

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Книги з теми "Seasonal Artificial Neural Network":

1

Sahai, A. K. Reduction of AGCM systematic error by artificial neural network: A new approach for dynamical seasonal prediction of Indian summer monsoon rainfall. Pune: [Indian Institute of Tropical Meteorology], 2000.

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2

Shanmuganathan, Subana, and Sandhya Samarasinghe, eds. Artificial Neural Network Modelling. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8.

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3

S, Mohan. Artificial neural network modelling. Roorkee: Indian National Committee on Hydrology, 2007.

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4

Zeidenberg, Matthew. Neural network models in artificial intelligence. New York: E. Horwood, 1990.

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5

Roberts, S. G. The evolution of artificial neural network structures. Manchester: UMIST, 1997.

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6

Bisi, Manjubala, and Neeraj Kumar Goyal. Artificial Neural Network for Software Reliability Prediction. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119223931.

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7

Rzempoluck, Edward J. Neural Network Data Analysis Using SimulnetTM. New York, NY: Springer New York, 1998.

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8

Kattan, Ali. Artificial neural network training and software implementation techniques. Hauppauge, N.Y: Nova Science Publishers, 2011.

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9

Kattan, Ali. Artificial neural network training and software implementation techniques. Hauppauge, N.Y: Nova Science Publishers, 2011.

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10

North Atlantic Treaty Organization. Advisory Group for Aerospace Research and Development. Artificial neural network approaches in guidance and control. Neuilly sur Seine, France: AGARD, 1991.

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Частини книг з теми "Seasonal Artificial Neural Network":

1

Sharma, Arjun, Anirban Mitra, Sumit Sharma, and Sudip Roy. "Estimation of Air Quality Index from Seasonal Trends Using Deep Neural Network." In Artificial Neural Networks and Machine Learning – ICANN 2018, 511–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_50.

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2

Dudek, Grzegorz. "Forecasting Time Series with Multiple Seasonal Cycles Using Neural Networks with Local Learning." In Artificial Intelligence and Soft Computing, 52–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38658-9_5.

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3

Amoo, Oseni Taiwo, Hammed Olabode Ojugbele, Abdultaofeek Abayomi, and Pushpendra Kumar Singh. "Hydrological Dynamics Assessment of Basin Upstream–Downstream Linkages Under Seasonal Climate Variability." In African Handbook of Climate Change Adaptation, 2005–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_116.

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AbstractThe impacts of climate change are already being felt, not only in terms of increase in temperature but also in respect of inadequate water availability. The Mkomazi River Basins (MRB) of the KwaZulu-Natal region, South Africa serves as major source of water and thus a mainstay of livelihood for millions of people living downstream. It is in this context that the study investigates water flows abstraction from headwaters to floodplains and how the water resources are been impacted by seasonal climate variability. Artificial Neural Network (ANN) pattern classifier was utilized for the seasonal classification and subsequence hydrological flow regime prediction between the upstream–downstream anomalies. The ANN input hydroclimatic data analysis results covering the period 2008–2015 provides a likelihood forecast of high, near-median, or low streamflow. The results show that monthly mean water yield range is 28.6–36.0 m3/s over the Basin with a coefficient of correlation (CC) values of 0.75 at the validation stage. The yearly flow regime exhibits considerable changes with different magnitudes and patterns of increase and decrease in the climatic variables. No doubt, added activities and processes such as land-use change and managerial policies in upstream areas affect the spatial and temporal distribution of available water resources to downstream regions. The study has evolved an artificial neuron system thinking from conjunctive streamflow prediction toward sustainable water allocation planning for medium- and long-term purposes.

4

Shekhar, Shashi, and Hui Xiong. "Artificial Neural Network." In Encyclopedia of GIS, 31. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_72.

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Majumder, Mrinmoy, and Apu K. Saha. "Artificial Neural Network." In Feasibility Model of Solar Energy Plants by ANN and MCDM Techniques, 13–16. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-308-8_4.

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6

Khazaii, Javad. "Artificial Neural Network." In Advanced Decision Making for HVAC Engineers, 145–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33328-1_14.

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Asadollahfardi, Gholamreza. "Artificial Neural Network." In SpringerBriefs in Water Science and Technology, 77–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44725-3_5.

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Daniel, Gómez González. "Artificial Neural Network." In Encyclopedia of Sciences and Religions, 143. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_200980.

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Okwu, Modestus O., and Lagouge K. Tartibu. "Artificial Neural Network." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications, 133–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_14.

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Attew, David. "Artificial Neural Network." In Perspectives in Neural Computing, 157–66. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0151-2_18.

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Тези доповідей конференцій з теми "Seasonal Artificial Neural Network":

1

Prochazka, A. "Neural networks and seasonal time-series prediction." In Fifth International Conference on Artificial Neural Networks. IEE, 1997. http://dx.doi.org/10.1049/cp:19970698.

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2

Khandelwal, Ina, Udit Satija, and Ratnadip Adhikari. "Forecasting seasonal time series with Functional Link Artificial Neural Network." In 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2015. http://dx.doi.org/10.1109/spin.2015.7095387.

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3

Meng, Bin, Xiao-li Xi, and Jie Li. "ASF seasonal correction of Loran-C based on artificial neural network." In NAECON 2009 - IEEE National Aerospace and Electronics Conference. IEEE, 2009. http://dx.doi.org/10.1109/naecon.2009.5426607.

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4

Asimakopoulou, Fani E., Georgios J. Tsekouras, Ioannis F. Gonos, and Ioannis A. Stathopulos. "Artificial neural network approach on the seasonal variation of soil resistance." In 2011 7th Asia-Pacific International Conference on Lightning (APL). IEEE, 2011. http://dx.doi.org/10.1109/apl.2011.6110235.

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5

Chen, Pudi, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, and Xueqi Cheng. "NeuCast: Seasonal Neural Forecast of Power Grid Time Series." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/460.

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Анотація:

In the smart power grid, short-term load forecasting (STLF) is a crucial step in scheduling and planning for future load, so as to improve the reliability, cost, and emissions of the power grid. Different from traditional time series forecast, STLF is a more challenging task, because of the complex demand of active and reactive power from numerous categories of electrical loads and the effects of environment. Therefore, we propose NeuCast, a seasonal neural forecasting method, which dynamically models various loads as co-evolving time series in a hidden space, as well as extra weather conditions, in a neural network structure. NeuCast captures seasonality and patterns of the time series by integrating factor modeling and hidden state recognition. NeuCast can also detect anomalies and forecast under different temperature assumptions. Extensive experiments on 134 real-word datasets show the improvements of NeuCast over the stateof-the-art methods.

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S. F. Ferraz, Rafael, Renato S. F. Ferraz, Lucas F. S. Azeredo, and Benemar A. de Souza. "Data Preprocessing for Load Forecasting using Artificial Neural Network." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2459.

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An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.

7

Saberian, Fatemeh, Ali Zamani, Mohammad Mahdi Gooya, Payman Hemmati, Mahdi Aliyari Shoorehdeli, and Mohammad Teshnehlab. "Prediction of seasonal influenza epidemics in Tehran using artificial neural networks." In 2014 22nd Iranian Conference on Electrical Engineering (ICEE). IEEE, 2014. http://dx.doi.org/10.1109/iraniancee.2014.6999855.

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8

Chen, Yuxuan, and Patrick Phelan. "Predicting Peak Energy Demand for an Office Building Using Artificial Intelligence (AI) Approaches." In ASME 2021 Power Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/power2021-64492.

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Abstract Due to the technological advancement in smart buildings and the smart grid, there is increasing desire of managing energy demand in buildings to achieve energy efficiency. In this context, building energy prediction has become an essential approach for measuring building energy performance, assessing energy system efficiency, and developing energy management strategies. In this study, two artificial intelligence techniques (i.e., ANN = artificial neural networks and SVR = support vector regression) are examined and used to predict the peak energy demand to estimate the energy usage for an office building on a university campus based on meteorological and historical energy data. Two-year energy and meteorological data are used, with one year for training and the following year for testing. To investigate the seasonal load trend and the prediction capabilities of the two approaches, two experiments are conducted relying on different scales of training data. In total, 10 prediction models are built, with 8 models implemented on seasonal training datasets and 2 models employed using year-round training data. It is observed that a backpropagation neural network (BPNN) performs better than SVR when dealing with more data, leading to stable generalization and low prediction error. When dealing with less data, it is found that there is no dominance of one approach over another.

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"Capability of Artificial Neural Networks for predicting long-term seasonal rainfalls in east Australia." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l8.mekanik2.

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Ahmad, A. S. "Estimation of salt contamination level on the high voltage insulators' surfaces during rainy season using artificial neural network." In Fifth International Conference on Power System Management and Control. IEE, 2002. http://dx.doi.org/10.1049/cp:20020052.

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Звіти організацій з теми "Seasonal Artificial Neural Network":

1

Powell, Bruce C. Artificial Neural Network Analysis System. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada392390.

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2

Karakowski, Joseph A., and Hai H. Phu. A Fuzzy Hypercube Artificial Neural Network Classifier. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada354805.

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3

Sgurev, Vassil. Artificial Neural Networks as a Network Flow with Capacities. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, September 2018. http://dx.doi.org/10.7546/crabs.2018.09.12.

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4

Vitela, J. E., U. R. Hanebutte, and J. Reifman. An artificial neural network controller for intelligent transportation systems applications. Office of Scientific and Technical Information (OSTI), April 1996. http://dx.doi.org/10.2172/219376.

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5

Vela, Daniel. Forecasting latin-american yield curves: an artificial neural network approach. Bogotá, Colombia: Banco de la República, March 2013. http://dx.doi.org/10.32468/be.761.

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6

Hsieh, Bernard B., and Charles L. Bartos. Riverflow/River Stage Prediction for Military Applications Using Artificial Neural Network Modeling. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada382991.

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Huang, Wenrui, and Catherine Murray. Application of an Artificial Neural Network to Predict Tidal Currents in an Inlet. Fort Belvoir, VA: Defense Technical Information Center, March 2003. http://dx.doi.org/10.21236/ada592255.

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Fitch, J. The radon transform for data reduction, line detection, and artificial neural network preprocessing. Office of Scientific and Technical Information (OSTI), May 1990. http://dx.doi.org/10.2172/6874873.

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Reifman, Jaques, and Javier Vitela. Artificial Neural Network Training with Conjugate Gradients for Diagnosing Transients in Nuclear Power Plants. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/10198077.

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Lee, Dongwon. Application of artificial neural network to prompt gamma neutron activation analysis for chemical warfare agents identification. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1565918.

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