Готові списки джерел за темами / Cascade-forward neural network

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Добірка наукової літератури з теми "Cascade-forward neural network"

Автор: Grafiati
Опубліковано: 3 червня 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "Cascade-forward neural network"

1

Alrubaie, Shymaa Akram Hantoush. "Cascade-Forward Neural Network for Volterra Integral Equation Solution." Ibn AL- Haitham Journal For Pure and Applied Sciences 34, no. 3 (2021): 104–15. http://dx.doi.org/10.30526/34.3.2683.

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Анотація:
The method of solving volterra integral equation by using numerical solution is a simple operation but to require many memory space to compute and save the operation. The importance of this equation appeares new direction to solve the equation by using new methods to avoid obstacles. One of these methods employ neural network for obtaining the solution.
 This paper presents a proposed method by using cascade-forward neural network to simulate volterra integral equations solutions. This method depends on training cascade-forward neural network by inputs which represent the mean of volterra
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2

Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Анотація:
Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action r
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3

Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892–99. https://doi.org/10.11591/ijeecs.v25.i2.pp892-899.

Повний текст джерела
Анотація:
Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action r
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4

Alkhasawneh, Mutasem Sh. "Hybrid Cascade Forward Neural Network with Elman Neural Network for Disease Prediction." Arabian Journal for Science and Engineering 44, no. 11 (2019): 9209–20. http://dx.doi.org/10.1007/s13369-019-03829-3.

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5

Warsito, Budi, Rukun Santoso, Suparti, and Hasbi Yasin. "Cascade Forward Neural Network for Time Series Prediction." Journal of Physics: Conference Series 1025 (May 2018): 012097. http://dx.doi.org/10.1088/1742-6596/1025/1/012097.

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6

Aribowo, Widi, Bambang Suprianto, I. Gusti Putu Asto Buditjahjanto, Mahendra Widyartono, and Miftahur Rohman. "An Improved Neural Network Based on Parasitism – Predation Algorithm for an Automatic Voltage Regulator." ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, no. 2 (2021): 136–44. http://dx.doi.org/10.37936/ecti-eec.2021192.241628.

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Анотація:
The parasitism – predation algorithm (PPA) is an optimization method that duplicates the interaction of mutualism between predators (cats), parasites (cuckoos), and hosts (crows). The study employs a combination of the PPA methods using the cascade-forward backpropagation neural network. This hybrid method employs an automatic voltage regulator (AVR) on a single machine system, with the performance measurement focusing on speed and the rotor angle. The performance of the proposed method is compared with the feed-forward backpropagation neural network (FFBNN), cascade-forward backpropagation ne
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7

Morinaga, Yuya, and Kazunori Yamaguchi. "Improvement of Neural Reverse Dictionary by Using Cascade Forward Neural Network." Journal of Information Processing 28 (2020): 715–23. http://dx.doi.org/10.2197/ipsjjip.28.715.

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8

Dhiaeddine, Mostefaoui Mohamed, and Benmouiza Khalil. "Optimal artificial neural network configurations for hourly solar irradiation estimation." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (2023): 4878–85. https://doi.org/10.11591/ijece.v13i5.pp4878-4885.

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Анотація:
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigat
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9

Kaveh, M., and R. A. Chayjan. "Mathematical and neural network modelling of terebinth fruit under fluidized bed drying." Research in Agricultural Engineering 61, No. 2 (2016): 55–65. http://dx.doi.org/10.17221/56/2013-rae.

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Анотація:
The paper presents an application which uses Feed Forward Neural Networks (FFNNs) to model the non-linear behaviour of the terebinth fruit drying. Mathematical models and Artificial Neural Networks (ANNs) were used for prediction of effective moisture diffusivity, specific energy consumption, shrinkage, drying rate and moisture ratio in terebinth fruit. Feed Forward Neural Network (FFBP) and Cascade Forward Neural Network (CFNN) as well as training algorithms of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used. Air temperature and velocity limits were 40–80&deg
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10

Wigati, Ekky Rosita Singgih, Budi Warsito, and Rita Rahmawati. "PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT." Jurnal Gaussian 7, no. 1 (2018): 64–72. http://dx.doi.org/10.14710/j.gauss.v7i1.26636.

Повний текст джерела
Анотація:
Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 uni
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Більше джерел

Дисертації з теми "Cascade-forward neural network"

1

Přecechtěl, Roman. "Optimalizace řízení aktivního síťového prvku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218166.

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The thesis deals with the use of neuronal networks for the control of telecommunication network elements. The aim of the thesis is to create a simulation model of network element with switching array with memory, in which the optimization kontrol switching array is solved by means of the neural network. All source code is created in integrated environment MATLAB. To training are used feed-forward backpropagation network. Miss achieve satisfactory result mistakes. Work apposite decision procedure given to problem and it is possible on ni tie up in an effort to find optimum solving.
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2

Waugh, SG. "Extending and benchmarking Cascade-Correlation : extensions to the Cascade-Correlation architecture and benchmarking of feed-forward supervised artificial neural networks." Thesis, 1995. https://eprints.utas.edu.au/21965/1/whole_WaughSamuelGeorge1997_thesis.pdf.

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Анотація:
This thesis is divided into two parts: the first examines various extensions to Cascade-Correlation, and the second examines the benchmarking of feed-forward supervised artificial neural networks, including back-propagation and Cascade-Correlation. The first extensions to the training mechanism of Cascade-Correlation involve the inclusion of patience to stop the addition of hidden nodes and the introduction of alternative methods for training the candidate pool. These methods greatly improve the training speed of the algorithm. Secondly, reducing the number of connections within Cascade-Corre
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Частини книг з теми "Cascade-forward neural network"

1

Annu Agarwal, Ajay Kumar Sharma, and Sarika Khandelwal. "Fingerprint Recognition System by Termination Points Using Cascade-Forward Backpropagation Neural Network." In Proceedings of the International Congress on Information and Communication Technology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0755-2_22.

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2

Wadkar, D. V., and A. S. Kote. "Applications of Cascade Feed Forward Neural Network for Modelling of Coagulant Dose in a Drinking Water Treatment Plant: Comparative Study." In Groundwater and Water Quality. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09551-1_14.

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3

Gupta, Rahul, and P. C. Gupta. "Cognitive Radio Networks Implementation for Optimum Spectrum Utilization Through Cascade Forward and Elman Backpropagation Neural Networks." In Proceedings of International Conference on Data Science and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6631-6_45.

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4

Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. "Feed Forward Neural Networks." In Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.003.0007.

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Анотація:
In this section, we want to give a brief introduction to neural networks (NNs). It is written for readers who are not familiar with neural networks but are curious about how they can be applied to practical problems in chemical reaction dynamics. The field of neural networks covers a very broad area. It is not possible to discuss all types of neural networks. Instead, we will concentrate on the most common neural network architecture, namely, the multilayer perceptron (MLP). We will describe the basics of this architecture, discuss its capabilities, and show how it has been used on several dif
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Тези доповідей конференцій з теми "Cascade-forward neural network"

1

Abdul Aziz, M. A., N. Ismail, I. M. Yassin, A. Zabidi, and M. S. A. Megat Ali. "Agarwood oil quality classification using cascade-forward neural network." In 2015 IEEE 6th Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2015. http://dx.doi.org/10.1109/icsgrc.2015.7412475.

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2

Khan, Idris, Honglu Zhu, Danish Khan, and Manoj Kumar Panjwani. "Photovoltaic power prediction by cascade forward artificial neural network." In 2017 International Conference on Information and Communication Technologies (ICICT). IEEE, 2017. http://dx.doi.org/10.1109/icict.2017.8320179.

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3

Shoumy, N. J., S. N. Yaakob, P. Ehkan, Md S. Ali, and S. Khatun. "Cascade-forward neural network performance study for bloodstain image analysis." In 2016 3rd International Conference on Electronic Design (ICED). IEEE, 2016. http://dx.doi.org/10.1109/iced.2016.7804646.

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4

Hananto, April Lia, Murtalim, Amir, et al. "Cascade-Forward Neural Network (CFNN) for biomass heating value prediction." In 5TH INTERNATIONAL CONFERENCE ON INNOVATIVE DESIGN, ANALYSIS & DEVELOPMENT PRACTICES IN AEROSPACE & AUTOMOTIVE ENGINEERING: I-DAD’22. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0141752.

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5

Saini, Satish, and Ritu Vijay. "Mammogram Analysis Using Feed-Forward Back Propagation and Cascade-Forward Back Propagation Artificial Neural Network." In 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2015. http://dx.doi.org/10.1109/csnt.2015.78.

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Yao-ming, Zhou, Meng Zhi-jun, Chen Xu-zhi, and Wu Zhe. "Helicopter engine performance prediction based on cascade-forward process neural network." In 2012 IEEE Conference on Prognostics and Health Management (PHM). IEEE, 2012. http://dx.doi.org/10.1109/icphm.2012.6299515.

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Mahmudah, Norma, Ardyono Priyadi, Avian Lukman Setya Budi, and Vita Lystianingrum Budiharto Putri. "Photovoltaic Power Forecasting Using Cascade Forward Neural Network Based On Levenberg-Marquardt Algorithm." In 2021 IEEE International Conference in Power Engineering Application (ICPEA). IEEE, 2021. http://dx.doi.org/10.1109/icpea51500.2021.9417842.

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Dhanaseely, A. J., S. Himavathi, and E. Srinivasan. "Performance comparison of cascade and feed forward neural network for face recognition system." In International Conference on Software Engineering and Mobile Application Modelling and Development (ICSEMA 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/ic.2012.0154.

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Sahrin, Alfin, Anang Tjahjono, Margo Pujiantara, and Mauridhi Hery Purnomo. "The modeling of directional overcurrent relay in loop system using cascade forward neural network." In 2017 International Seminar on Intelligent Technology and its Applications (ISITIA). IEEE, 2017. http://dx.doi.org/10.1109/isitia.2017.8124057.

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Thilagavathi, M., and S. Abirami. "Cascade-Forward Neural Network in Identification of Plant Species of Desert Based on Wild Flowers." In 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2018. http://dx.doi.org/10.1109/icscan.2018.8541172.

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