Relevant bibliographies by topics / Feed Forward Neural Network (FFNN)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Feed Forward Neural Network (FFNN)'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Feed Forward Neural Network (FFNN)"

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Hasbi, Yasin, Warsito Budi, and Santoso Rukun. "Feed Forward Neural Network Modeling for Rainfall Prediction." E3S Web of Conferences 73 (2018): 05017. http://dx.doi.org/10.1051/e3sconf/20187305017.

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Prediction of rainfall data by using Feed Forward Neural Network (FFNN) model is proposed. FFNN is a class of neural network which has three layers for processing. In time series prediction, including in case of rainfall data, the input layer is the past values of the same series up to certain lag and the output layer is the current value. Beside a few lagged times, the seasonal pattern also considered as an important aspect of choosing the potential input. The autocorrelation function and partial autocorrelation function patterns are used as aid of selecting the input. In the second layer cal
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Aribowo, Widi, Supari Muslim, Fendi Achmad, and Aditya Chandra Hermawan. "Improving Neural Network Based on Seagull Optimization Algorithm for Controlling DC Motor." Jurnal Elektronika dan Telekomunikasi 21, no. 1 (August 31, 2021): 48. http://dx.doi.org/10.14203/jet.v21.48-54.

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This article presents a direct current (DC) motor control approach using a hybrid Seagull Optimization Algorithm (SOA) and Neural Network (NN) method. SOA method is a nature-inspired algorithm. DC motor speed control is very important to maintain the stability of motor operation. The SOA method is an algorithm that duplicates the life of the seagull in nature. Neural network algorithms will be improved using the SOA method. The neural network used in this study is a feed-forward neural network (FFNN). This research will focus on controlling DC motor speed. The efficacy of the proposed method i
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Aldakheel, Fadi, Ramish Satari, and Peter Wriggers. "Feed-Forward Neural Networks for Failure Mechanics Problems." Applied Sciences 11, no. 14 (July 14, 2021): 6483. http://dx.doi.org/10.3390/app11146483.

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This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward neural network (FFNN)—are utilized directly for the development of the failure model. The verification and generalization of the trained models for elasticity as well as fracture behavior are investigated by several representative numerical examples under different loading condit
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Dwi Prasetyo, Mohammad Imron, Anang Tjahjono, and Novie Ayub Windarko. "FEED FORWARD NEURAL NETWORK SEBAGAI ALGORITMA ESTIMASI STATE OF CHARGE BATERAI LITHIUM POLYMER." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 7, no. 1 (March 2, 2020): 13. http://dx.doi.org/10.20527/klik.v7i1.290.

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<p><em>Estimasi State Of Charge (SOC) baterai merupakan parameter terpenting dalam Battery Management System (BMS), terlebih sebagai aplikasi dari mobil listrik dan smart grid. SOC tidak dapat dilakukan pengukuran secara langsung, sehingga diperlukan metode estimasi untuk mendapatkan nilai tersebut. Beberapa metode yang pernah diusulkan adalah coloumb counting dan open circuit voltage. Akan tetapi coloumb counting memiliki kelemahan dalam hal inisialisasi SOC awal dan memiliki ketergantungan terhadap sensor arus. Sedangkan metode open circuit voltage hanya dapat digunakan pada bate
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S K. Dhakad, S. K. Dhakad, Dr S. C. soni Dr. S.C.soni, and Dr Pankaj Agrawal. "The feed forward neural network (FFNN) based model prediction of Molten Carbonate Fuel cells (MCFCs)." Indian Journal of Applied Research 3, no. 2 (October 1, 2011): 142–43. http://dx.doi.org/10.15373/2249555x/feb2013/49.

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Novickis, Rihards, Daniels Jānis Justs, Kaspars Ozols, and Modris Greitāns. "An Approach of Feed-Forward Neural Network Throughput-Optimized Implementation in FPGA." Electronics 9, no. 12 (December 18, 2020): 2193. http://dx.doi.org/10.3390/electronics9122193.

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Artificial Neural Networks (ANNs) have become an accepted approach for a wide range of challenges. Meanwhile, the advancement of chip manufacturing processes is approaching saturation which calls for new computing solutions. This work presents a novel approach of an FPGA-based accelerator development for fully connected feed-forward neural networks (FFNNs). A specialized tool was developed to facilitate different implementations, which splits FFNN into elementary layers, allocates computational resources and generates high-level C++ description for high-level synthesis (HLS) tools. Various top
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Zainudin, Fathin Liyana, Sharifah Saon, Abd Kadir Mahamad, Musli Nizam Yahya, Mohd Anuaruddin Ahmadon, and Shingo Yamaguchi. "Feed forward neural network application for classroom reverberation time estimation." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (July 1, 2019): 346. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp346-354.

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<span>Acoustic problem is a main issues of the existing classroom due to lack of absorption of surface material. Thus, a feed forward neural network system (FFNN) for classroom Reverberation Time (RT) estimation computation was built. This system was developed to assist the acoustic engineer and consultant to treat and reduce this matter. Data was collected and computed using ODEON12.10 ray tracing method, resulting in a total of 600 rectangular shaped classroom models that were modeled with various length, width, height, as well as different surface material types. The system is able to
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Aribowo, Widi, Bambang Suprianto, and Joko Joko. "Improving neural network using a sine tree-seed algorithm for tuning motor DC." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (June 1, 2021): 1196. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp1196-1204.

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A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is a
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Sallam, Tarek, Ahmed Attiya, and Nada El-Latif. "Neural-Network-Based Multiobjective Optimizer for Dual-Band Circularly Polarized Antenna." Applied Computational Electromagnetics Society 36, no. 3 (April 20, 2021): 252–58. http://dx.doi.org/10.47037/2020.aces.j.360304.

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A multiobjective optimization (MOO) technique for a dual-band circularly polarized antenna by using neural networks (NNs) is introduced in this paper. In particular, the optimum antenna dimensions are computed by modeling the problem as a multilayer feed-forward neural network (FFNN), which is two-stage trained with I/O pairs. The FFNN is chosen because of its characteristic of accurate approximation and good generalization. The data for FFNN training is obtained by using HFSS EM simulator by varying different geometrical parameters of the antenna. A two strip-loaded circular aperture antenna
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Cloud, Kirkwood A., Brian J. Reich, Christopher M. Rozoff, Stefano Alessandrini, William E. Lewis, and Luca Delle Monache. "A Feed Forward Neural Network Based on Model Output Statistics for Short-Term Hurricane Intensity Prediction." Weather and Forecasting 34, no. 4 (July 24, 2019): 985–97. http://dx.doi.org/10.1175/waf-d-18-0173.1.

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Abstract A feed forward neural network (FFNN) is developed for tropical cyclone (TC) intensity prediction, where intensity is defined as the maximum 1-min average 10-m wind speed. This deep learning model incorporates a real-time operational estimate of the current intensity and predictors derived from Hurricane Weather Research and Forecasting (HWRF; 2017 version) Model forecasts. The FFNN model is developed with the operational constraint of being restricted to 6-h-old HWRF data. Best track intensity data are used for observational verification. The forecast training data are from 2014 to 20
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Dissertations / Theses on the topic "Feed Forward Neural Network (FFNN)"

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Khanna, Neha, and Neha Khanna@mdbc gov au. "Investigation of phytoplankton dynamics using time-series analysis of biophysical parameters in Gippsland Lakes, South-eastern Australia." RMIT University. Civil, Environmental and Chemical Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080226.123435.

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There is a need for ecological modelling to help understand the dynamics in ecological systems, and thus aid management decisions to maintain or improve the quality of the ecological systems. This research focuses on non linear statistical modelling of observations from an estuarine system, Gippsland Lakes, on the south-eastern coast of Australia. Feed forward neural networks are used to model chlorophyll time series from a fixed monitoring station at Point King. The research proposes a systematic approach to modelling in ecology using feed forward neural networks, to ensure: (a) that res
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Hadjiprocopis, Andreas. "Feed forward neural network entities." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340374.

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Tanaka, Toshiyuki. "Control of growth dynamics of feed-forward neural network." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/13445.

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Al-Mudhaf, Ali F. "A feed forward neural network approach for matrix computations." Thesis, Brunel University, 2001. http://bura.brunel.ac.uk/handle/2438/5010.

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A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to
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Richards, Gareth D. "Implementation and capabilities of layered feed-forward networks." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/11313.

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Mohammadi, Mohammad Mehdi. "PREDICTION OF WIND TURBINE BLADE FATIGUE LOADS USING FEED-FORWARD NEURAL NETWORKS." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444115.

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In recent years, machine learning applications have gained great attention in the wind power industry. Among these, artificial neural networks have been utilized to predict the fatigue loads of wind turbine components such as rotor blades. However, the limited number of contributions and differences in the used databases give rise to several questions which this study has aimed to answer. Therefore, in this study, 5-min SCADA data from the Lillgrund wind farm has been used to train two feed-forward neural networks to predict the fatigue loads at the blade root in flapwise and edgewise directio
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Nyman, Jacob. "Machinery Health Indicator Construction using Multi-objective Genetic Algorithm Optimization of a Feed-forward Neural Network based on Distance." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298084.

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Assessment of machine health and prediction of future failures are critical for maintenance decisions. Many of the existing methods use unsupervised techniques to construct health indicators by measuring the disparity between the current state and either the healthy or the faulty states of the system. This approach can work well, but if the resulting health indicators are insufficient there is no easy way to steer the algorithm towards better ones. In this thesis a new method for health indicator construction is investigated that aims to solve this issue. It is based on measuring distance afte
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Nigrini, L. B., and G. D. Jordaan. "Short term load forecasting using neural networks." Journal for New Generation Sciences, Vol 11, Issue 3: Central University of Technology, Free State, Bloemfontein, 2013. http://hdl.handle.net/11462/646.

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Published Article<br>Several forecasting models are available for research in predicting the shape of electric load curves. The development of Artificial Intelligence (AI), especially Artificial Neural Networks (ANN), can be applied to model short term load forecasting. Because of their input-output mapping ability, ANN's are well-suited for load forecasting applications. ANN's have been used extensively as time series predictors; these can include feed-forward networks that make use of a sliding window over the input data sequence. Using a combination of a time series and a neural network pr
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Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.

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Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictio
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Gróf, Zoltán. "Realizace rozdělujících nadploch." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219781.

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The main aim of this master's thesis is to describe the subject of the implementation of decision boundaries with the help of artificial neural networks. The objective is to present theoretical knowledge concerning this field and on practical examples prove these statements. The work contains basic theoretical description of the field of pattern recognition and the field of feature based representation of objects. A classificator working on the basis of Bayes decision is presented in this part, and other types of classificators are named as well. The work then deals with artificial neural netw
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Book chapters on the topic "Feed Forward Neural Network (FFNN)"

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Kingdon, Jason. "Feed-Forward Neural Network Modelling." In Perspectives in Neural Computing, 37–53. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0949-5_3.

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Hadjiprocopis, Andreas, and Peter Smith. "Feed Forward Neural Network entities." In Biological and Artificial Computation: From Neuroscience to Technology, 349–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032493.

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Sher, Gene I. "Developing a Feed Forward Neural Network." In Handbook of Neuroevolution Through Erlang, 153–85. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4463-3_6.

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Ferrán, Edgardo A., and Roberto P. J. Perazzo. "Symmetry and representability properties of feed-forward neural networks." In International Neural Network Conference, 792. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_90.

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Lisa, F., J. Carrabina, C. Pérez-Vicente, N. Avellana, and E. Valderrama. "Feed forward network for vehicle license character recognition." In New Trends in Neural Computation, 638–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56798-4_214.

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Kumar, P. N., G. Rahul Seshadri, A. Hariharan, V. P. Mohandas, and P. Balasubramanian. "Financial Market Prediction Using Feed Forward Neural Network." In Communications in Computer and Information Science, 77–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20209-4_11.

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Müller, Peter, and David Rios Insua. "Posterior Simulation for Feed Forward Neural Network Models." In COMPSTAT, 385–90. Heidelberg: Physica-Verlag HD, 1996. http://dx.doi.org/10.1007/978-3-642-46992-3_51.

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Skansi, Sandro. "Modifications and Extensions to a Feed-Forward Neural Network." In Undergraduate Topics in Computer Science, 107–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73004-2_5.

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Kotwal, Adit, Jai Kotia, Rishika Bharti, and Ramchandra Mangrulkar. "Training a Feed-Forward Neural Network Using Cuckoo Search." In Springer Tracts in Nature-Inspired Computing, 101–22. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5163-5_5.

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Milosevic, Stefan, Timea Bezdan, Miodrag Zivkovic, Nebojsa Bacanin, Ivana Strumberger, and Milan Tuba. "Feed-Forward Neural Network Training by Hybrid Bat Algorithm." In Modelling and Development of Intelligent Systems, 52–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68527-0_4.

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Conference papers on the topic "Feed Forward Neural Network (FFNN)"

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Dambrosio, Lorenzo, Marco Bomba, Sergio M. Camporeale, and Bernardo Fortunato. "Feed Forward Neural Network-Based Diagnostic Tool for Gas Turbine Power Plant." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30019.

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A diagnostic tool able to detect faults that may occur in a gas turbine power plant at an early stage of their emergence is of a great importance for power production. In the present paper, a diagnostic tool, based on Feed Forward Neural Networks (FFNN), has been proposed for gas turbine power plants with a condition monitoring approach. The main aim of the proposed diagnostic tool is to reliably detect not only every considered single fault, but also two or more faults that may occur contemporarily. Two different FFNNs compose the proposed diagnostic tool. The first network, that is not-fully
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Weerasinghe, Y. S. P., M. W. P. Maduranga, and M. B. Dissanayake. "RSSI and Feed Forward Neural Network (FFNN) Based Indoor Localization in WSN." In 2019 National Information Technology Conference (NITC). IEEE, 2019. http://dx.doi.org/10.1109/nitc48475.2019.9114515.

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Camporeale, S., L. Dambrosio, A. Milella, M. Mastrovito, and B. Fortunato. "Fault Diagnosis of Combined Cycle Gas Turbine Components Using Feed Forward Neural Networks." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38742.

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A diagnostic tool based on Feed Forward Neural Networks (FFNN) is proposed to detect the origin of performance degradation in a Combined Cycle Gas Turbine (CCGT) power plant. In such a plant, due the connection of the steam cycle to the gas turbine, any deterioration of gas turbine components affects not only the gas turbine itself but also the steam cycle. At the same time, fouling of the heat recovery boiler may cause the increase of the turbine back-pressure, reducing the gas turbine performance. Therefore, measurements taken from the steam cycle can be included in the fault variable set, u
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Adege, Abebe Belay, Lei Yen, Hsin-piao Lin, Yirga Yayeh, Yun Ruei Li, Shiann-Shiun Jeng, and Getaneh Berie. "Applying Deep Neural Network (DNN) for large-scale indoor localization using feed-forward neural network (FFNN) algorithm." In 2018 IEEE International Conference on Applied System Innovation (ICASI). IEEE, 2018. http://dx.doi.org/10.1109/icasi.2018.8394387.

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Fullerton, Anne M., Thomas C. Fu, and David E. Hess. "Investigation and Prediction of Wave Impact Loads on Ship Appendage Shapes." In ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29217.

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Navy fleet problems with damage to hatches and other appendages after operation in high sea states suggest that wave impact loads may be greater than the current design guidelines of 1000 pounds per square foot (48 kilopascal) (Ship Specification Section 100, General Requirements for Hull Structure and Guidance Manual for Temporary Alterations, NAVSEA S9070-AA-MME-010/SSN, SSBN). These large impact forces not only cause damage to ships and ship structures, they can also endanger the ship’s crew. To design robust marine structures, accurate estimates of all encountered loads are necessary, incl
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Devi, Bharathi B. "Probabilistic feed-forward neural network." In Photonics for Industrial Applications, edited by David P. Casasent. SPIE, 1994. http://dx.doi.org/10.1117/12.188906.

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Rosay, Arnaud, Florent Carlier, and Pascal Leroux. "Feed-forward neural network for Network Intrusion Detection." In 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring). IEEE, 2020. http://dx.doi.org/10.1109/vtc2020-spring48590.2020.9129472.

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Zhao, Huiqing. "Neural Network Blind Equalization Algorithm Based on Feed Forward Neural Network." In First International Conference on Information Science and Electronic Technology (ISET 2015). Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/iset-15.2015.31.

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Tamura. "On interpretations of a feed-forward neural network." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118350.

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Zhou, Wengang, Leiting Dong, Lubomir Bic, Mingtian Zhou, and Leiting Chen. "Internet traffic classification using feed-forward neural network." In 2011 International Conference on Computational Problem-Solving (ICCP). IEEE, 2011. http://dx.doi.org/10.1109/iccps.2011.6092257.

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Reports on the topic "Feed Forward Neural Network (FFNN)"

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automati
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