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

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

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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Journal, Baghdad Science. "On Training Of Feed Forward Neural Networks." Baghdad Science Journal 4, no. 1 (March 4, 2007): 158–64. http://dx.doi.org/10.21123/bsj.4.1.158-164.

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In this paper we describe several different training algorithms for feed forward neural networks(FFNN). In all of these algorithms we use the gradient of the performance function, energy function, to determine how to adjust the weights such that the performance function is minimized, where the back propagation algorithm has been used to increase the speed of training. The above algorithms have a variety of different computation and thus different type of form of search direction and storage requirements, however non of the above algorithms has a global properties which suited to all problems.

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Raju, Paladugu, Veera Malleswara Rao, and Bhima Prabhakara Rao. "Grey Wolf Optimization-Based Artificial Neural Network for Classification of Kidney Images." Journal of Circuits, Systems and Computers 27, no. 14 (August 23, 2018): 1850231. http://dx.doi.org/10.1142/s0218126618502316.

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Ultrasound (US) imaging is the initial phase in the preliminary diagnosis for the treatment of kidney diseases, particularly to estimate kidney size, shape and position, to give information about kidney function, and to help in diagnosis of abnormalities like cysts, stones, junctional parenchyma and tumors which is shown in Figs. 7–9. This study proposes Grey Level Co-occurrence Matrix (GLCM)-based Probabilistic Principal Component Analysis (PPCA) and Artificial Neural Network (ANN) method for the classification of kidney images. Grey Wolf Optimization (GWO) is used to update the current posit

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Fuangkhon, Piyabute. "Parallel Distance-Based Instance Selection Algorithm for Feed-Forward Neural Network." Journal of Intelligent Systems 26, no. 2 (April 1, 2017): 335–58. http://dx.doi.org/10.1515/jisys-2015-0039.

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AbstractInstance selection endeavors to decide which instances from the data set should be maintained for further use during the learning process. It can result in increased generalization of the learning model, shorter time of the learning process, or scaling up to large data sources. This paper presents a parallel distance-based instance selection approach for a feed-forward neural network (FFNN), which can utilize all available processing power to reduce the data set while obtaining similar levels of classification accuracy as when the original data set is used. The algorithm identifies the

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Utama, Faisal Fikri, Budi Warsito, and Sugito Sugito. "MODEL FEED FORWARD NEURAL NETWORK (FFNN) DENGAN ALGORITMA PARTICLE SWARM SEBAGAI OPTIMASI BOBOT (Studi Kasus : Harga Daging Sapi dari Bank Dunia Periode Januari 2007 – Desember 2018)." Jurnal Gaussian 8, no. 1 (February 28, 2019): 117–26. http://dx.doi.org/10.14710/j.gauss.v8i1.26626.

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Beef is one of the important food commodities to fulfill the nutritional adequacy of humans. The World Bank notes the beef prices that are exported worldwide every month. For this reason, those data becomes a predictable series for the next period. Feed Forward Neural Network is a non-parametric method that can be used to make predictions from time series data without having to be bound by classical assumptions. The initiated weight will be evaluated by an algorithm that can minimize errors. Particle Swarm Optimization (PSO) is an optimization algorithm based on particle speed to reach the des

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Edupuganti, Sirisha, Ravichandra Potumarthi, Thadikamala Sathish, and Lakshmi Narasu Mangamoori. "Role of Feed Forward Neural Networks Coupled with Genetic Algorithm in Capitalizing of Intracellular Alpha-Galactosidase Production byAcinetobactersp." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/361732.

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Alpha-galactosidase production in submerged fermentation byAcinetobactersp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE

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Gerek, Ibrahim Halil, Ercan Erdis, Gulgun Mistikoglu, and Mumtaz Usmen. "MODELLING MASONRY CREW PRODUCTIVITY USING TWO ARTIFICIAL NEURAL NETWORK TECHNIQUES." Journal of Civil Engineering and Management 21, no. 2 (October 22, 2014): 231–38. http://dx.doi.org/10.3846/13923730.2013.802741.

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Artificial neural networks have been effectively used in various civil engineering fields, including construction management and labour productivity. In this study, the performance of the feed forward neural network (FFNN) was compared with radial basis neural network (RBNN) in modelling the productivity of masonry crews. A variety of input factors were incorporated and analysed. Mean absolute percentage error (MAPE) and correlation coefficient (R) were used to evaluate model performance. Research results indicated that the neural computing techniques could be successfully employed in modellin

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Santoso, H., and D. Murdianto. "Analisis Pengenalan Bendera Negara Rumpun Melayu Menggunakan Metode Feed Forward Neural Network." Jurnal Teknologi dan Informasi 10, no. 2 (September 1, 2020): 144–52. http://dx.doi.org/10.34010/jati.v10i2.2702.

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Telah dilakukan analisis pada sistem pengenalan gambar empat buah bendera negara rumpun melayu secara digital. Negara tersebut adalah Indonesia, Malaysia, Singapura, dan Brunei Darussalam. Tujuan dari penelitian ini adalah sebagai bentuk langkah awal dalam melatih sistem Artificial Intelligence (Kecerdasan Buatan) dalam membedakan empat buah negara rumpun melayu berdasarkan warna dan motif bendera pada sebuah peta digital. Proses analisis dan pelatihan pengenalan bendera tersebut menggunakan metode Feed Forward Neural Network (FFNN). Hasilnya menunjukkan bahwa penggunaan 4 buah Hidden Layer, s

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Najdet Nasret Coran, Ali, Prof Dr Hayri Sever, and Dr Murad Ahmed Mohammed Amin. "Acoustic data classification using random forest algorithm and feed forward neural network." International Journal of Engineering & Technology 9, no. 2 (July 1, 2020): 582. http://dx.doi.org/10.14419/ijet.v9i2.30815.

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Speaker identification systems are designed to recognize the speaker or set of speakers according to their acoustic analysis. Many approach-es are made to perform the acoustic analysis in the speech signal, the general description of those systems is time and frequency domain analysis. In this paper, acoustic information is extracted from the speech signals using MFCC and Fundamental Frequency methods combi-nation. The results are classified using two different algorithms such as Random-forest and Feed Forward Neural Network. The FFNN classifier integration with the acoustic model resulted a r

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Naganathan, G. S., and C. K. Babulal. "Voltage Stability Margin Assessment Using Multilayer Feed Forward Neural Network." Applied Mechanics and Materials 573 (June 2014): 661–67. http://dx.doi.org/10.4028/www.scientific.net/amm.573.661.

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With the deregulation of electricity markets, the system operation strategies have changed in recent years. The systems are operated with smaller margins. How to maintain the voltage stability of the power systems have become an important issue.This paper presents an Artificial Feed Forward Neural Network (FFNN) approach for the assessment of power system voltage stability. This paper uses some input feature sets using real power, reactive power, voltage magnitude and phase angle to train the neural network (NN). The target output for each input pattern is obtained by computing the distance to

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Awadalla, M., H. Yousef, A. Al-Shidani, and A. Al-Hinai. "Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 12 (September 23, 2016): 7263–83. http://dx.doi.org/10.24297/ijct.v15i12.4354.

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This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has beenÂ accomplished on the achieved results to validate the modelsâ€™ pred

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Sondhiya, D. K., S. K. Kasde, Dishansh Raj Upwar, and A. K. Gwal. "Identification of Very Low Frequency (VLF) Whistlers transients using Feed Forward Neural Network (FFNN)." IOSR Journal of Applied Physics 09, no. 04 (July 2017): 23–29. http://dx.doi.org/10.9790/4861-0904012329.

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Bhandarkar, Tanvi, Vardaan K, Nikhil Satish, S. Sridhar, R. Sivakumar, and Snehasish Ghosh. "Earthquake trend prediction using long short-term memory RNN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1304. http://dx.doi.org/10.11591/ijece.v9i2.pp1304-1312.

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<p>The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Netw

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Khatib, Tamer, Azah Mohamed, K. Sopian, and M. Mahmoud. "Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction." International Journal of Photoenergy 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/946890.

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This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction. The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN). The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Prediction results show that the GRNN exceeds the other proposed methods. The average values of the MAP

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Bezabeh, Belete Biazen, and Abrham Debasu Mengistu. "The effects of multiple layers feed-forward neural network transfer function in digital based Ethiopian soil classification and moisture prediction." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 4073. http://dx.doi.org/10.11591/ijece.v10i4.pp4073-4079.

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In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectifie

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Akşahin, Mehmet, Aykut Erdamar, Hikmet Fırat, Sadık Ardıç, and Osman Eroğul. "OBSTRUCTIVE SLEEP APNEA CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORK BASED ON TWO SYNCHRONIC HRV SERIES." Biomedical Engineering: Applications, Basis and Communications 27, no. 02 (March 17, 2015): 1550011. http://dx.doi.org/10.4015/s1016237215500118.

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In the present study, "obstructive sleep apnea (OSA) patients" and "non-OSA patients" were classified into two groups using with two synchronic heart rate variability (HRV) series obtained from electrocardiography (ECG) and photoplethysmography (PPG) signals. A linear synchronization method called cross power spectrum density (CPSD), commonly used on HRV series, was performed to obtain high-quality signal features to discriminate OSA from controls. To classify simultaneous sleep ECG and PPG signals recorded from OSA and non-OSA patients, various feed forward neural network (FFNN) architectures

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Ansari, Saniya, and Udaysingh Sutar. "Devanagari Handwritten Character Recognition using Hybrid Features Extraction and Feed Forward Neural Network Classifier (FFNN)." International Journal of Computer Applications 129, no. 7 (November 17, 2015): 22–27. http://dx.doi.org/10.5120/ijca2015906859.

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Hanafaie, Affan, Sugito Sugito, and Sudarno Sudarno. "PERAMALAN MENGGUNAKAN MODEL FEED FORWARD NEURAL NETWORK DENGAN ALGORITMA ADAPTIVE SIMULATED ANNEALING (Studi kasus: Harga minyak mentah dunia yang dipublikasikan oleh OPEC)." Jurnal Gaussian 7, no. 4 (November 30, 2018): 373–84. http://dx.doi.org/10.14710/j.gauss.v7i4.28865.

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Today, crude oil trading industry is still an important industry in the world because it still has high fuel oil consumption. The crude oil prices tend to fluctuate causing the prediction of crude oil in the coming periods to be a challenge. Forecasting the price of crude oil can be done by various methods, one of them is ARIMA Box-Jenkins model with OLS method to estimate the parameter, but this method has several assumptions that must be met. As time goes by, many methods that discovered, one of them is artificial neural network which can combined with various parameter optimization methods

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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 (February 28, 2018): 64–72. http://dx.doi.org/10.14710/j.gauss.v7i1.26636.

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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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C, Narmatha. "A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs." Journal of Computational Science and Intelligent Technologies 1, no. 3 (2020): 1–8. http://dx.doi.org/10.53409/mnaa.jcsit20201301.

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The Wireless Sensor Networks (WSNs) are vulnerable to numerous security hazards that could affect the entire network performance, which could lead to catastrophic problems such as a denial of service attacks (DoS). The WSNs cannot protect these types of attacks by key management protocols, authentication protocols, and protected routing. A solution to this issue is the intrusion detection system (IDS). It evaluates the network with adequate data obtained and detects the sensor node(s) abnormal behavior. For this work, it is proposed to use the intrusion detection system (IDS), which recognizes

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Et al., Al-Saif. "Solving Mixed Volterra - Fredholm Integral Equation (MVFIE) by Designing Neural Network." Baghdad Science Journal 16, no. 1 (March 11, 2019): 0116. http://dx.doi.org/10.21123/bsj.2019.16.1.0116.

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In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency an

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Boujoudar, Younes, Hassan Elmoussaoui, and Tijani Lamhamdi. "Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (October 1, 2019): 3415. http://dx.doi.org/10.11591/ijece.v9i5.pp3415-3422.

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<span class="fontstyle0">In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The pro

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Gaya, Muhammad Sani, Norhaliza Abdul Wahab, Yahya M. Sam, Azna N. Anuar, and Sharatul Izah Samsuddin. "ANFIS Modelling of Carbon Removal in Domestic Wastewater Treatment Plant." Applied Mechanics and Materials 372 (August 2013): 597–601. http://dx.doi.org/10.4028/www.scientific.net/amm.372.597.

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Modelling of an ill-defined system such as the wastewater treatment plant is quite tedious and difficult. However, successful and optimal operation of the system relied upon a suitable model. Most of the available developed models were applied to industrial wastewater treatment plants. This paper presents adaptive neuro fuzzy inference system (ANFIS) model for carbon removal in the Bunu domestic wastewater treatment plant in Kuala Lumpur, Malaysia. For comparison feed-forward neural network (FFNN) was used. Simulation results revealed that ANFIS model is slightly better than the FFNN model, th

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Dada, Emmanuel Gbenga, Hurcha Joseph Yakubu, and David Opeoluwa Oyewola. "Artificial Neural Network Models for Rainfall Prediction." European Journal of Electrical Engineering and Computer Science 5, no. 2 (April 2, 2021): 30–35. http://dx.doi.org/10.24018/ejece.2021.5.2.313.

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Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Netwo

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Jayasankar, T., and J. Arputha Vijayaselvi. "Prediction of Syllable Duration Using Structure Optimised Cuckoo Search Neural Network (SOCNN) for Text-To-Speech." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7538–44. http://dx.doi.org/10.1166/jctn.2016.5750.

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A Feed Forward Neural Network (FFNN) model primarily based unrestricted delivery prediction of language unit length pattern info speech synthesis system is that the focus of this paper. Estimation of delivery parameter of segmental length plays a essential half in unrestricted concatenative synthesis Text To Speech System (TTS) is capable of synthesize natural sounding speech with improved quality. Common options to coach the Neural Network enclosed language unit position within the phrase, context of language unit, language unit position within the word, language unit nucleus and amp; languag

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Yasin, Hasbi, Budi Warsito, Rukun Santoso, and Arief Rachman Hakim. "Forecasting of Rainfall in Central Java using Hybrid GSTAR-NN-PSO Model." E3S Web of Conferences 125 (2019): 23015. http://dx.doi.org/10.1051/e3sconf/201912523015.

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Forecasting of rainfall trends is essential for several fields, such as airline and ship management, flood control and agriculture. The rainfall data were recorded several time simultaneously at a number of locations and called the space-time data. Generalized Space Time Autoregressive (GSTAR) model is one of space-time models used to modeling and forecasting the rainfall. The aim of this research is to propose the nonlinear space-time model based on hybrid of GSTAR, Feed Forward Neural Network (FFNN) and Particle Swarm Optimization (PSO) and it called GSTAR-NN-PSO. In this model, input variab

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Kişi, Özgür. "River flow forecasting and estimation using different artificial neural network techniques." Hydrology Research 39, no. 1 (February 1, 2008): 27–40. http://dx.doi.org/10.2166/nh.2008.026.

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This paper demonstrates the application of different artificial neural network (ANN) techniques for the estimation of monthly streamflows. In the first part of the study, three different ANN techniques, namely, feed forward neural networks (FFNN), generalized regression neural networks (GRNN) and radial basis ANN (RBF) are used in one-month ahead streamflow forecasting and the results are evaluated. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in the Eastern Black Sea region of Turkey are used in the study. Based on the results

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Kumar, Keshav, Vivekanand Singh, and Thendiyath Roshni. "Efficacy of hybrid neural networks in statistical downscaling of precipitation of the Bagmati River basin." Journal of Water and Climate Change 11, no. 4 (July 26, 2019): 1302–22. http://dx.doi.org/10.2166/wcc.2019.259.

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Abstract This study investigates and analyses the present and future senarios of precipitation using statistical downscaling techniques at selected sites of the Bagmati River basin. Statistical downscaling is achieved by feed forward neural network (FFNN) and wavelet neural network (WNN) models. Potential predictors for the model development are selected based on the performances of Pearson product moment correlation and factor analysis. Different training algorithms are compared and the traincgb training algorithm is selected for development of FFNN and WNN models. The visual comparison and t

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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 Decompos

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Awadalla, Medhat, and Hassan Yousef. "Neural Networks for Flow Bottom Hole Pressure Prediction." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (August 1, 2016): 1839. http://dx.doi.org/10.11591/ijece.v6i4.10774.

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Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps. However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowin

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Awadalla, Medhat, and Hassan Yousef. "Neural Networks for Flow Bottom Hole Pressure Prediction." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 4 (August 1, 2016): 1839. http://dx.doi.org/10.11591/ijece.v6i4.pp1839-1856.

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Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps. However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowin

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Mapuwei, Tichaona W., Oliver Bodhlyera, and Henry Mwambi. "Univariate Time Series Analysis of Short-Term Forecasting Horizons Using Artificial Neural Networks: The Case of Public Ambulance Emergency Preparedness." Journal of Applied Mathematics 2020 (May 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/2408698.

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This study examined the applicability of artificial neural network models in modelling univariate time series ambulance demand for short-term forecasting horizons in Zimbabwe. Bulawayo City Councils’ ambulance services department was used as a case study. Two models, feed-forward neural network (FFNN) and seasonal autoregressive integrated moving average, (SARIMA) were developed using monthly historical data from 2010 to 2017 and compared against observed data for 2018. The mean absolute error (MAE), root mean square error (RMSE), and paired sample t-test were used as performance measures. Cal

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Verma, Hari Om, and Naba Kumar Peyada. "Aircraft parameter estimation using ELM network." Aircraft Engineering and Aerospace Technology 92, no. 6 (May 1, 2020): 895–907. http://dx.doi.org/10.1108/aeat-01-2019-0003.

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Purpose The purpose of this paper is to investigate the estimation methodology with a highly generalized cost-effective single hidden layer neural network. Design/methodology/approach The aerodynamic parameter estimation is a challenging research area of aircraft system identification, which finds various applications such as flight control law design and flight simulators. With the availability of the large database, the data-driven methods have gained attention, which is primarily based on the nonlinear function approximation using artificial neural networks. A novel single hidden layer feed

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Theodoropoulos, Panayiotis, Christos C. Spandonidis, Nikos Themelis, Christos Giordamlis, and Spilios Fassois. "Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power." Journal of Marine Science and Engineering 9, no. 2 (January 24, 2021): 116. http://dx.doi.org/10.3390/jmse9020116.

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Adverse conditions within specific offshore environments magnify the challenges faced by a vessel’s energy-efficiency optimization in the Industry 4.0 era. As the data rate and volume increase, the analysis of big data using analytical techniques might not be efficient, or might even be infeasible in some cases. The purpose of this study is the development of deep-learning models that can be utilized to predict the propulsion power of a vessel. Two models are discriminated: (1) a feed-forward neural network (FFNN) and (2) a recurrent neural network (RNN). Predictions provided by these models w

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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 (June 2, 2016): 55–65. http://dx.doi.org/10.17221/56/2013-rae.

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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&ndash;80&deg

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Telchy, Fatin. "Intelligent Feedback Scheduling of Control Tasks." Iraqi Journal for Electrical and Electronic Engineering 10, no. 2 (December 1, 2014): 64–79. http://dx.doi.org/10.37917/ijeee.10.2.2.

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An efficient feedback scheduling scheme based on the proposed Feed Forward Neural Network (FFNN) scheme is employed to improve the overall control performance while minimizing the overhead of feedback scheduling which exposed using the optimal solutions obtained offline by mathematical optimization methods. The previously described FFNN is employed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. The proposed intelligent scheduler will be examined with different optimization algorithms. An inverted pendulum cost functi

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Firat, M. "Artificial Intelligence Techniques for river flow forecasting in the Seyhan River Catchment, Turkey." Hydrology and Earth System Sciences Discussions 4, no. 3 (June 6, 2007): 1369–406. http://dx.doi.org/10.5194/hessd-4-1369-2007.

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Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), for forecasting of daily river flow is investigated and the Seyhan catchment, located in the south of Turkey, is chosen as a case study. Totally, 5114 daily river flow data are obtained from river flow gauges station of Üçtepe

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Kote, A. S., and D. V. Wadkar. "Modeling of Chlorine and Coagulant Dose in a Water Treatment Plant by Artificial Neural Networks." Engineering, Technology & Applied Science Research 9, no. 3 (June 8, 2019): 4176–81. http://dx.doi.org/10.48084/etasr.2725.

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Coagulation and chlorination are complex processes of a water treatment plant (WTP). Determination of coagulant and chlorine dose is time-consuming. Many times WTP operators in India determine the coagulant and chlorine dose approximately using their experience, which may lead to the use of excess or insufficient dose. Hence, there is a need to develop prediction models to determine optimum chlorine and coagulant doses. In this paper, artificial neural networks (ANN) are used for prediction due to their ability to learn and model non-linear and complex relationships. Separate ANN models for ch

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Oladele, Adewole, Vera Vokolkova, and Jerome Egwurube. "Transportation Planning through Pavement Performance Prediction Modeling for Botswana Gravel loss Condition." Applied Mechanics and Materials 256-259 (December 2012): 2976–82. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2976.

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Botswana is a Southern African country with an area of about 582,000 sq. km and its small population of about 2 million people. The road transportation network has grown beyond all expectations since independence in 1966. Out of the 18,300 km Botswana Public Highway Networks, gravel road networks are significant in providing access to rural areas where the majority of the population lives. Modelling of gravel loss conditions are required in order to predict their conditions in the future and provide information on the manner in which pavements perform. Such information can be applied to transp

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Zaidan, Martha A., Ola Surakhi, Pak Lun Fung, and Tareq Hussein. "Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters." Sensors 20, no. 10 (May 19, 2020): 2876. http://dx.doi.org/10.3390/s20102876.

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Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descripto

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Babu, Sangita. "A Hybrid Approach for Intrusion Detection using OPSO and Hybridization of Feed Forward Neural Network (FFNN) with Probabilistic Neural Network (PNN)- HFFPNN Classifier." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1 (February 15, 2020): 206–10. http://dx.doi.org/10.30534/ijatcse/2020/31912020.

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