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Journal articles on the topic 'Batch Back-propagation algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Al Duais, Mohammed Sarhan, and Fatma Susilawati Mohamad. "Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training." Journal of Telecommunications and Information Technology 4 (December 20, 2017): 82–89. http://dx.doi.org/10.26636/jtit.2017.113017.

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The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suﬀers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most signiﬁcant parameter for increasing the eﬃciency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is pres

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MOHAMMED, SARHAN AL_DUAIS, A.G. AL KHULAIDI ABDUALMAJED, SUSILAWATI. MOHAMAD FATMA, et al. "AUTO-ADAPTIVE THE WEIGHT IN BATCH BACK PROPAGATION ALGORITHM VIA DYNAMIC LEARNING RATE." Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) 42, no. 09 (2023): 131–45. https://doi.org/10.5281/zenodo.8348528.

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<strong>Abstract</strong> Batch back propagation (BBP) algorithm is commonly used in many applications, including robotics, automation, and global positioning systems. The man drawbacks of batch back propagation (BBP) algorithm is slow training, and there are several parameters needs to be adjusted manually, also suffers from saturation training. The objective of this study is to improve the speed uptraining of the BBP algorithm and to remove the saturation training. To overcome these problems, we have created a new dynamic learning rate to escape the local minimum, which enables a faster trai

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Jayakumar, Santhakumar, Sathish Kannan, Poongavanam Ganeshkumar, and U. Mohammed Iqbal. "Reinventing the Trochoidal Toolpath Pattern by Adaptive Rounding Radius Loop Adjustments for Precision and Performance in End Milling Operations." Journal of Manufacturing and Materials Processing 9, no. 6 (2025): 171. https://doi.org/10.3390/jmmp9060171.

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The present work intends to assess the impact of trochoidal toolpath rounding radius loop adjustments on surface roughness, nose radius wear, and resultant cutting force during end milling of AISI D3 steel. Twenty experimental trials have been performed utilizing a face-centered central composite design through a response surface approach. Artificial Neural Network (ANN) models were built to forecast outcomes, utilizing four distinct learning algorithms: the Batch Back Propagation Algorithm (BBP), Quick Propagation Algorithm (QP), Incremental Back Propagation Algorithm (IBP), and Levenberg–Mar

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Zhang, Huisheng, Wei Wu, and Mingchen Yao. "Boundedness and convergence of batch back-propagation algorithm with penalty for feedforward neural networks." Neurocomputing 89 (July 2012): 141–46. http://dx.doi.org/10.1016/j.neucom.2012.02.029.

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Nandani, E. J. K. P., and T. T. S. Vidanapathirana. "Forecasting Paddy Yield in Sri Lanka Using Back-propagation Learning in Artificial Neural Network Model." Journal of the University of Ruhuna 12, no. 2 (2024): 110–20. https://doi.org/10.4038/jur.v12i2.8032.

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Climate change has a direct and indirect impact on food production, and food production depends on the available resources such as the quantity of seeds sown, climate, soil moisture, solar radiation, expected carbon, fertilizers, pesticides, government policies, etc. Paddy yield is one of the major contributors to food production, and there are several studies on forecasting paddy yield production in Sri Lanka using common algorithms in ANNs, this study focused on forecasting the paddy yield in Sri Lanka based on some climate factors using the selected best steepest descent optimizer algorithm

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Saranya, N., and Priya S. Kavi. "Deep Convolutional Neural Network Feed-Forward and Back Propagation (DCNN-FBP) Algorithm for Predicting Heart Disease using Internet of Things." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 1 (2021): 83–87. https://doi.org/10.35940/ijeat.A3212.1011121.

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In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP al

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N, Saranya, and Kavi Priya S. "Deep Convolutional Neural Network Feed Forward and Back Prop a gation (DCNN F BP) Algorithm f or Predicting Heart Disease u sing Internet o f Things." International Journal of Engineering and Advanced Technology 11, no. 1 (2021): 283–87. http://dx.doi.org/10.35940/ijeat.a3212.1011121.

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In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP al

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Mohammed, Sarhan Al_Duais, and Susilawati Mohamad Fatma. "Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor." International Journal of Artificial Intelligence (IJ-AI) 7, no. 4 (2018): 170–78. https://doi.org/10.11591/ijai.v7.i4.pp170-178.

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The man problem of batch back propagation (BBP) algorithm is slow training and there are several parameters needs to be adjusted manually, also suffers from saturation training. The learning rate and momentum factor are significant parameters for increasing the efficiency of the (BBP). In this study, we created a new dynamic function of each learning rate and momentum facor. We present the DBBPLM algorithm, which trains with a dynamic function for each the learning rate and momentum factor. A Sigmoid function used as activation function. The XOR problem, balance, breast cancer and iris dataset

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Al_Duais, Mohammed Sarhan, and Fatma Susilawati Mohamad. "Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 4 (2018): 170. http://dx.doi.org/10.11591/ijai.v7.i4.pp170-178.

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<span lang="EN-US">The man problem of batch back propagation (BBP) algorithm is slow training and there are several parameters needs to be adjusted manually, also suffers from saturation training.</span><span lang="EN-US">The learning rate and momentum factor are significant parameters for increasing the efficiency of the (BBP). In this study, we created a new dynamic function of each learning rate and momentum facor. We present the DBBPLM algorithm, which trains with a dynamic function for each the learning rate and momentum factor.<br /> A Sigmoid function used as act

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Hu, Zhang, and Wei Qin. "Fuzzy Method and Neural Network Model Parallel Implementation of Multi-Layer Neural Network Based on Cloud Computing for Real Time Data Transmission in Large Offshore Platform." Polish Maritime Research 24, s2 (2017): 39–44. http://dx.doi.org/10.1515/pomr-2017-0062.

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Abstract With the rapid development of electronic technology, network technology and cloud computing technology, the current data is increasing in the way of mass, has entered the era of big data. Based on cloud computing clusters, this paper proposes a novel method of parallel implementation of multilayered neural networks based on Map-Reduce. Namely in order to meet the requirements of big data processing, this paper presents an efficient mapping scheme for a fully connected multi-layered neural network, which is trained by using error back propagation (BP) algorithm based on Map-Reduce on c

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Abdollahi, Yadollah, Azmi Zakaria, Nor Asrina Sairi, et al. "Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/726101.

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The artificial neural network (ANN) modeling ofm-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration ofm-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets

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Aalipour, Mehdi, Bohumil Šťastný, Filip Horký, and Bahman Jabbarian Amiri. "Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments." Water 14, no. 6 (2022): 920. http://dx.doi.org/10.3390/w14060920.

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Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The

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Krishna, D., Kumar G. Santhosh, and Raju D. R. Prasada. "Artificial neural network (ANN) approach for modeling of lead (II) adsorption from wastewater using a ragi husk powder." i-manager’s Journal on Future Engineering and Technology 17, no. 2 (2022): 1. http://dx.doi.org/10.26634/jfet.17.2.18553.

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The main sources of lead in the environment are effluent industries such as electroplating, alloying, smelting, mining, refining, pigmenting, plastic manufacture, and metallurgical industries. A batch experiment as well as an Artificial Neural Network (ANN) coupled with a Genetic Algorithm model for the extraction of lead from wastewater was conducted. In the development of the ANN model, a tan sigmoid transfer function for input and a purelin for output layers have been employed. A feed-forward back propagation with a single layer was used with thirteen neurons in the hidden layer. The optimi

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Yu, Wenxin, Shoudao Huang, and Weihong Xiao. "Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System." Energies 11, no. 10 (2018): 2561. http://dx.doi.org/10.3390/en11102561.

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To investigate problems involving wind turbines that easily occur but are hard to diagnose, this paper presents a wind turbine (WT) fault diagnosis algorithm based on a spectrogram and a convolutional neural network. First, the original data are sampled into a phonetic form. Then, the data are transformed into a spectrogram in the time-frequency domain. Finally, the data are sent into a convolutional neural network (CNN) model with batch regularization for training and testing. Experimental results show that the method is suitable for training a large number of samples and has good scalability

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He, Ping, Yiwei Fan, Banglong Pan, Yinfeng Zhu, Jing Liu, and Darong Zhu. "Calibration and Verification of Dynamic Particle Flow Parameters by the Back-Propagation Neural Network Based on the Genetic Algorithm: Recycled Polyurethane Powder." Materials 12, no. 20 (2019): 3350. http://dx.doi.org/10.3390/ma12203350.

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The discrete element method (DEM) is commonly used to study various powders in motion during transportation, screening, mixing, etc.; this requires several microscopic parameters to characterize the complex mechanical behavior of the particles. Herein, a new discrete element parameter calibration method is proposed to calibrate the ultrafine agglomerated powder (recycled polyurethane powder). Optimal Latin hypercube sampling and virtual simulation experiments were conducted using the commercial DEM software; the microscopic variables included the static friction coefficient between the particl

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Krishna, D., and Sree R. Padma. "Modeling of Chromium (VI) adsorption on limonia acidissima hull powder using Artificial Neural Network (ANN) approach." i-manager's Journal on Chemical Sciences 2, no. 1 (2020): 32. http://dx.doi.org/10.26634/jchem.2.1.17441.

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Batch experiment was carried out for the removal of Chromium (VI) from aqueous solution to get experimental data to which an Artificial Neural Network (ANN) model was developed using 16 experimental data points (for testing) and 36 experimental data points (for training). A single layer feed forward back propagation was used to get minimum mean square error with eleven neurons in hidden layer. To develop ANN model, a tan sigmoid transfer function for input and purelin for output layers were used. The optimized process parameters viz., adsorbent dosage, pH and initial concentration of chromium

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Wang, Leijie, Xudong Guo, Qiuyue Peng, et al. "Prediction and accuracy improvement of insulin pump in-fusion deviation based on LSTM and PID." PLOS One 20, no. 6 (2025): e0324261. https://doi.org/10.1371/journal.pone.0324261.

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In order to further improve the injection precision of the PH300 insulin pump, this paper optimizes and improves the mechanical structure and control algorithm of the PH300. The improved PH300 uses a proportional-integral-derivative controller based on back propagation neural network (BP-PID) algorithm to control operation, and the experimental results show that the minimum effective single infusion dose of the improved PH300 is 0.047 U, which is reduced by 50.52%. The deviation reduction of low-dose infusion (0.1U-0.9U) ranged from 1.47% to 10.87%, with a mean of 4.91%. The mean deviation of

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Liu, Yang, Xiang Li, Xianbang Chen, Xi Wang, and Huaqiang Li. "High-Performance Machine Learning for Large-Scale Data Classification considering Class Imbalance." Scientific Programming 2020 (May 18, 2020): 1–16. http://dx.doi.org/10.1155/2020/1953461.

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Currently, data classification is one of the most important ways to analysis data. However, along with the development of data collection, transmission, and storage technologies, the scale of the data has been sharply increased. Additionally, due to multiple classes and imbalanced data distribution in the dataset, the class imbalance issue is also gradually highlighted. The traditional machine learning algorithms lack of abilities for handling the aforementioned issues so that the classification efficiency and precision may be significantly impacted. Therefore, this paper presents an improved

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Sebti, Aicha, Fatiha Souahi, Faroudja Mohellebi, and Sadek Igoud. "Experimental study and artificial neural network modeling of tartrazine removal by photocatalytic process under solar light." Water Science and Technology 76, no. 2 (2017): 311–22. http://dx.doi.org/10.2166/wst.2017.201.

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This research focuses on the application of an artificial neural network (ANN) to predict the removal efficiency of tartrazine from simulated wastewater using a photocatalytic process under solar illumination. A program is developed in Matlab software to optimize the neural network architecture and select the suitable combination of training algorithm, activation function and hidden neurons number. The experimental results of a batch reactor operated under different conditions of pH, TiO2 concentration, initial organic pollutant concentration and solar radiation intensity are used to train, va

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Kuo, Huang-Cheng, and Shih-Hao Chen. "Self-Organizing Map Learning with Momentum." Computer and Information Science 9, no. 1 (2016): 136. http://dx.doi.org/10.5539/cis.v9n1p136.

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<p class="zhengwen"><span lang="EN-GB">Self-organizing map (SOM) is a type of artificial neural network for cluster analysis. Each neuron in the map competes with others for the input data objects in order to learn the grouping of the input space. Besides competition, neighbor neurons of a winning neuron also learn. SOM has a natural propensity to cluster data into visually distinct clusters, which show the intrinsic grouping of data.</span></p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family:

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K M, Prof Ramya, Pavan H, Darshan Gowda, Bhagavantray Hosamani, and Jagadeva A S. "MULTIMODAL BIOMETRIC IDENTIFICATION SYSTEM USING THE FUSION OF FINGERPRINT AND IRIS RECOGNITION WITH CNN APPROACH." International Journal of Engineering Applied Sciences and Technology 6, no. 8 (2021): 213–20. http://dx.doi.org/10.33564/ijeast.2021.v06i08.036.

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Multimodal biometric systems are widely applied in many real-world applications because of its ability to accommodate variety of great limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, nonuniversality, and vulnerability to spoofing. during this paper, an efficient and real-time multimodal biometric system is proposed supported building deep learning representations for images of both the correct and left irises of someone, and fusing the results obtained employing a ranking-level fusion method. The trained deep learning sys

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Gowdru Chandrashekarappa, Manjunath Patel, Prasad Krishna, and Mahesh B. Parappagoudar. "Forward and Reverse Process Models for the Squeeze Casting Process Using Neural Network Based Approaches." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/293976.

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The present research work is focussed to develop an intelligent system to establish the input-output relationship utilizing forward and reverse mappings of artificial neural networks. Forward mapping aims at predicting the density and secondary dendrite arm spacing (SDAS) from the known set of squeeze cast process parameters such as time delay, pressure duration, squeezes pressure, pouring temperature, and die temperature. An attempt is also made to meet the industrial requirements of developing the reverse model to predict the recommended squeeze cast parameters for the desired density and SD

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El-Rabbany, Ahmed, and Mohamed El-Diasty. "A New Approach to Sequential Tidal Prediction." Journal of Navigation 56, no. 2 (2003): 305–14. http://dx.doi.org/10.1017/s0373463303002285.

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Accurate tide prediction is required for safe marine navigation in shallow waters as well as for other marine operations. Traditionally, tide prediction was carried out using the harmonic method, which is based on the identification of the harmonic tidal constituents existing in the tidal record. Unfortunately, however, unless long tidal records are available at the tide gauges, some important tidal constituents may not be identified. This, in turn, deteriorates the accuracy of the tidal prediction. More recently, a sequential least-squares prediction method capable of using relatively short t

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Kegang Lu. "Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)." Journal of Electrical Systems 20, no. 7s (2024): 1556–70. http://dx.doi.org/10.52783/jes.3734.

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Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterwa

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Alasl, M. Kashefi, M. Khosravi, M. Hosseini, G. R. Pazuki, and R. Nezakati Esmail Zadeh. "Measurement and mathematical modelling of nutrient level and water quality parameters." Water Science and Technology 66, no. 9 (2012): 1962–67. http://dx.doi.org/10.2166/wst.2012.333.

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Physico-chemical water quality parameters and nutrient levels such as water temperature, turbidity, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, conductivity, total nitrogen and total phosphorus, were measured from April to September 2011 in the Karaj dam area, Iran. Total nitrogen in water was modelled using an artificial neural network system. In the proposed system, water temperature, depth, saturated oxygen, dissolved oxygen, pH, chlorophyll-a, salinity, turbidity and conductivity were considered as input data, and the total nitrogen in water was considered as output. T

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Rayane, Karim, and Omar Allaoui. "Application of Artificial Neural Network for Prediction of Boride Layer Depth Obtained on XC38 Steel in Molten Salts." Defect and Diffusion Forum 365 (July 2015): 194–99. http://dx.doi.org/10.4028/www.scientific.net/ddf.365.194.

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This paper discusses an application of neural network system on the performance of boride layer thickness. Boriding treatment was carried out in three different molten salts consisting of borax (Na2B4O7) added to boron carbide (B4C), aluminum (Al) and silicon carbides (SiC). The substrate used in this study was XC38 steel. Borides layers involved in this work was obtained from a boriding treatment at the temperature range of 800-1050 °C with 50°C interval for 2, 4 and 6 h. A numerical experiment using normalized and binarized values was carried out, using a back-propagation algorithm in ANN. T

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Akkar, Suheila Abd Alreda, and Sawsan Abd Muslim Mohammed. "Design of Intelligent Network to Predicate Phenol Removal from Waste Water by Emulsion Liquid Membrane." Materials Science Forum 1021 (February 2021): 115–28. http://dx.doi.org/10.4028/www.scientific.net/msf.1021.115.

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This research introduced Intelligent Network's proposed design for predicting efficiency in the removal of phenol from wastewater by liquid membrane emulsion. In the inner phase of W / O emulsions, phenol extraction from an aqueous solution was investigated using emulsion liquid membrane prepared with kerosene as a membrane phase, Span 80 as a surfactant, and NaOH as a stripping agent. Experiments were conducted to investigate the effect of three emulsion composition variables, namely: surfactant concentration, membrane phase to-internal (VM / VI) volume ratio, and removal phase concentration

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EKINCI, ŞERAFETTIN, AHMET AKDEMIR, and HUMAR KAHRAMANLI. "MODELING AND INVESTIGATION OF THE WEAR RESISTANCE OF SALT BATH NITRIDED AISI 4140 VIA ANN." Surface Review and Letters 20, no. 03n04 (2013): 1350033. http://dx.doi.org/10.1142/s0218625x13500339.

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Nitriding is usually used to improve the surface properties of steel materials. In this way, the wear resistance of steels is improved. We conducted a series of studies in order to investigate the microstructural, mechanical and tribological properties of salt bath nitrided AISI 4140 steel. The present study has two parts. For the first phase, the tribological behavior of the AISI 4140 steel which was nitrided in sulfinuz salt bath (SBN) was compared to the behavior of the same steel which was untreated. After surface characterization using metallography, microhardness and sliding wear tests w

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Zhou, Ping, Gongbo Zhou, Zhencai Zhu, et al. "Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network." Applied Sciences 8, no. 8 (2018): 1346. http://dx.doi.org/10.3390/app8081346.

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With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is

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Son, Le Ngoc, Nguyen The Duc, Sumihiko Murata, and Phan Ngoc Trung. "Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data." Geofluids 2024 (January 11, 2024): 1–28. http://dx.doi.org/10.1155/2024/4097442.

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Developing automatic history matching (AHM) methods to replace the traditional manual history matching (MHM) approach in adjusting the permeability distribution of the reservoir simulation model has been studied by many authors. Because permeability values need to be evaluated at hundreds of thousands of grid cells in a typical reservoir simulation model, it is necessary to apply a reparameterization technique to allow the optimization algorithms to be implemented with fewer variables. In basic reparameterization techniques including zonation and pilot point methods, the calibrations are usual

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Abed, Ali, Abduladhem Ali, Nauman Aslam, and Ali Marhoon. "Fuzzy-Neural Petri Net Distributed Control System Using Hybrid Wireless Sensor Network and CAN Fieldbus." Iraqi Journal for Electrical and Electronic Engineering 12, no. 1 (2016): 54–70. http://dx.doi.org/10.37917/ijeee.12.1.6.

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The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless s

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Abiodun, M. Aibinu, J. E. Salami Momoh, A. Shafie Amir, and Rahman Najeeb Athaur. "Increasing The Speed of Convergence of an Artificial Neural Network based ARMA Coefficients Determination Technique." June 23, 2008. https://doi.org/10.5281/zenodo.1058333.

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In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reducti

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SAMPREET, K. R., VASAREDDY MAHIDHAR, R. KARTHIC NARAYANAN, and T. DEEPAN BHARATHI KANNAN. "Optimization of Process Parameters in Laser Welding of Hastelloy C-276 Using Artificial Neural Network and Genetic Algorithm." Surface Review and Letters, November 12, 2020, 2050042. http://dx.doi.org/10.1142/s0218625x20500420.

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In this paper, an effort is made to determine the optimized parameters in laser welding of Hastelloy C-276 using Artificial Neural Network (ANN) and Genetic Algorithm (GA). CO2 Laser welding was performed on a sheet of thickness 1.6[Formula: see text]mm based on Taguchi L27 orthogonal array. Laser power, welding speed and shielding gas flow rate were chosen as input parameters and Bead width, depth of Penetration and Microhardness were measured for assessing the weld quality. ANN was applied for modeling the welding process parameters i.e. heat input, welding speed and gas flow rate. Various l

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Sarhan Al_Duais, Mohammed, and F. S. Mohamad. "A Review on Enhancements to Speed up Training of the Batch Back Propagation Algorithm." Indian Journal of Science and Technology 9, no. 46 (2016). http://dx.doi.org/10.17485/ijst/2016/v9i46/91755.

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Mazouz, A., and C. P. Bridges. "Automated CNN back-propagation pipeline generation for FPGA online training." Journal of Real-Time Image Processing, July 23, 2021. http://dx.doi.org/10.1007/s11554-021-01147-2.

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AbstractTraining of convolutional neural networks (CNNs) on embedded platforms to support on-device learning has become essential for the future deployment of CNNs on autonomous systems. In this work, we present an automated CNN training pipeline compilation tool for Xilinx FPGAs. We automatically generate multiple hardware designs from high-level CNN descriptions using a multi-objective optimization algorithm that explores the design space by exploiting CNN parallelism. These designs that trade-off resources for throughput allow users to tailor implementations to their hardware and applicatio

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Zhu, Jikun, and Weili Xiong. "An improved transfer learning approach based on geodesic flow kernel for multiphase batch process soft sensor modeling." Transactions of the Institute of Measurement and Control, February 16, 2024. http://dx.doi.org/10.1177/01423312241229965.

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For multiphase batch process, the characteristics of process data under various batches differ. Consequently, the soft sensor model built for a particular working condition is inapplicable to other working conditions. Besides, each batch can be divided into several phases whose characteristics are probably different. To address the above problems, a soft sensor model based on phase division and transfer learning strategy is proposed. First, transfer learning strategy is adopted to construct a soft sensor model applicable to various working conditions. Specifically, geodesic flow kernel based o

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Sahu, Anshul. "Design and Implementation of MCNN for Better Prediction of Stock Price Movement." International Journal of Scientific Research in Science and Technology, December 10, 2018, 238–43. http://dx.doi.org/10.32628/cseit183877.

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The stock market prediction is problematic subsequently the stock price is active in environment. To decrease the inappropriate predictions of the stock market and evolution the ability to predict the market actions. To escape the risk and the challenging in predicting stock price. Predicting stock market prices is a difficult task that conventionally contains extensive neural network. Owed to the linked environment of stock prices, conventional batch processing technique cannot be developed competently for stock market analysis. We propose an efficient Learning algorithm that develops a kind

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Davoudi, Khatereh, and Parimala Thulasiraman. "Evolving convolutional neural network parameters through the genetic algorithm for the breast cancer classification problem." SIMULATION, March 5, 2021, 003754972199603. http://dx.doi.org/10.1177/0037549721996031.

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Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer mortality in women around the world. However, it can be controlled effectively by early diagnosis, followed by effective treatment. Clinical specialists take the advantages of computer-aided diagnosis (CAD) systems to make their diagnosis as accurate as possible. Deep learning techniques, such as the convolutional neural network (CNN), due to their classification capabilities on learned feature methods and ability of working with complex images, have been widely adopted in CAD systems. The parameters of the n

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Nohair, Mohamed, Noura Mallouk, Marouane Benmarzouk, and El Morrakchi Mohssine. "Statistical Approaches to Estimating the Relative Contribution of Intermolecular Interactions in Aliphatic Alcohols: Application to QSPR/QSAR Modeling of Their Boiling Points." Chemical Product and Process Modeling 4, no. 1 (2009). http://dx.doi.org/10.2202/1934-2659.1274.

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A three-layer feed forward neural network trained with a LevenbergMarquardt batch error back propagation algorithm has been used to model the strong relationships between the boiling point of aliphatic alcohols and intermolecular forces consisting both in Van Der Waals forces and polar interactions, respectively. For that purpose, we use the multifunctional autocorrelation method to provide an appropriate topological description. Two types of descriptors are generated: the first is commonly used in QSARs and QSPRs modelling, it gives a general description of the whole of the molecule; the sec

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"Verification of Biometric Traits using Deep Learning." International Journal of Innovative Technology and Exploring Engineering 8, no. 10S (2019): 452–59. http://dx.doi.org/10.35940/ijitee.j1083.08810s19.

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Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems including non-universality, noise, population coverage, vulnerability and intra-class variability for verification, authentication and identification of an individual. In this paper, the impact of deep learning in the field of biometrics is investigated where supervised learning is primarily involved in identifying biometric traits using Graphical User Interface. The trained deep learning system proposed is calle

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"Water level prediction by artificial neural network in a flashy transboundary river of Bangladesh." Issue 2 16, no. 2 (2014): 432–44. http://dx.doi.org/10.30955/gnj.001226.

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<p>This paper presents the sensitivity analysis results of feed forward multilayer perceptron based Artificial Neural Network model for water level prediction in a data constraint transnational Surma River of Bangladesh. Catchment characteristics, hydro-geomorphological, meteorological and headwater information of the upper catchment area are not available to the authors. As such past daily total rainfall and water levels data available within the country are utilized in this study. Logistic sigmoid activation function with unit steepness parameter is exercised for non-linear transformat

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"An Estimating Model for Water quality of river Ganga using Artificial Neural Network." International Journal of Innovative Technology and Exploring Engineering 8, no. 9 (2019): 1448–53. http://dx.doi.org/10.35940/ijitee.i7900.078919.

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In given propose paper we have worked on the water quality of river ganga, not only for management of water resources, but also for the prevention of water pollution, the water quality forecast has a more practical significance. To evolvesuitableideals for the water quality (WQ) insidethe water physiquesobtainingcontaminant samples & then to confirm that these standards are encountered, this is the environmentalWQ management program have goal. In the realistic standard setting, the institutional capacity of the basin’s water science, environmental, & the land usagesituations, possibleu

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Lin, Zhaoliang, and Jinguo Li. "FedEVCP: Federated Learning-Based Anomalies Detection for Electric Vehicle Charging Pile." Computer Journal, August 7, 2023. http://dx.doi.org/10.1093/comjnl/bxad078.

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Abstract Vehicle-to-Grid (V2G) is a technology that enables electric vehicles to use smart charging methods to harness low-cost and renewable energy when it is available, and obtain income by feeding energy back into the grid. With the rise of V2G technology, the use of electric vehicles has begun to increase dramatically, which relies on the reliable Electric Vehicle Charging Pile (EVCP). However, most EVCPs are online and networked, introducing many potential network threats, such as Electricity Theft, Identity Theft and False Data Injection etc. Prior work has mostly focused on machine lear

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