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Journal articles on the topic 'Error back propagation algorithm'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Singarimbun, Roy Nuary. "Adaptive Moment Estimation To Minimize Square Error In Backpropagation Algorithm." Data Science: Journal of Computing and Applied Informatics 4, no. 1 (2020): 27–46. http://dx.doi.org/10.32734/jocai.v4.i1-1160.

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Back - propagation Neural Network has weaknesses such as errors of gradient descent training slowly of error function, training time is too long and is easy to fall into local optimum. Back - propagation algorithm is one of the artificial neural network training algorithm that has weaknesses such as the convergence of long, over-fitting and easy to get stuck in local optima. Back - propagation is used to minimize errors in each iteration. This paper investigates and evaluates the performance of Adaptive Moment Estimation (ADAM) to minimize the squared error in back - propagation gradient desce

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SUN, Wei-wei. "Adaptive Back-Propagation algorithm with magnified error signals." Journal of Computer Applications 28, no. 8 (2008): 2081–83. http://dx.doi.org/10.3724/sp.j.1087.2008.02081.

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Felix, A. Pyatakovich* Lubov V. Khlivnenko Tatyana I. Yakunchenko Kristina F. Makkonen Olga V. Mevsha. "A COMPARATIVE ANALYSIS OF THE RESULTS OF CONVENTIONAL AND COMBINED METHODS OF TRAINING DIRECT PROPAGATION NEURAL NETWORK IN HEALTHY PERSONS TO DETECTING THE DEGREE OF ACTIVITY OF AN AUTONOMOUS NERVOUS SYSTEM." Indo American Journal of Pharmaceutical Sciences 04, no. 09 (2017): 3075–79. https://doi.org/10.5281/zenodo.910741.

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The article deals with a comparative analysis of the efficiency of an artificial neural network (ANN) trained with the help of algorithm back propagation, and of that trained by combining a back propagation algorithm and a variant of Cauchy stochastic training, in detecting the degree of activity of an autonomous nervous system. For the purposes realization of the project has been developed a biotechnical system, including technical device for input electrophysiological information in mode on-line. To evaluate the clinical effectiveness of the classification, records of interpulse intervals in

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Jiang, Yu Lian. "Natural Gas Load Forecasting Based on Improved Back Propagation Neural Network." Applied Mechanics and Materials 563 (May 2014): 312–15. http://dx.doi.org/10.4028/www.scientific.net/amm.563.312.

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To suit for the condition that the relative error is more popular than the absolute error, and overcome the shortcoming of the traditional Back propagation neural network, this paper proposed an improved Back propagation algorithm with additional momentum item based on the sum of relative error square. The improved algorithm was applied to the example of the natural gas load forecasting, simulations showed that the improved algorithm has faster training speed than the traditional algorithm, and has higher accuracy as while.

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Ororbia, Alexander G., and Ankur Mali. "Biologically Motivated Algorithms for Propagating Local Target Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4651–58. http://dx.doi.org/10.1609/aaai.v33i01.33014651.

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Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research. In this paper, we propose a learning algorithm called error-driven Local Representation Alignment (LRA-E), which has strong connections to predictive coding, a theory that offers a mechanistic way of describing neurocomputational machinery. In addition, we propose an improved variant of Difference Target Propagation, another procedure that comes from the same family of algorithms as LRA-E. We compare our procedures to several other biologically

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Oh, Sang-Hoon. "Error back-propagation algorithm for classification of imbalanced data." Neurocomputing 74, no. 6 (2011): 1058–61. http://dx.doi.org/10.1016/j.neucom.2010.11.024.

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Sarkar, Dilip. "Methods to speed up error back-propagation learning algorithm." ACM Computing Surveys 27, no. 4 (1995): 519–44. http://dx.doi.org/10.1145/234782.234785.

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Ma, Chi, Liang Zhao, Xuesong Mei, Hu Shi, and Jun Yang. "Thermal error compensation based on genetic algorithm and artificial neural network of the shaft in the high-speed spindle system." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 5 (2016): 753–67. http://dx.doi.org/10.1177/0954405416639893.

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To improve the accuracy, generality and convergence of thermal error compensation model based on traditional neural networks, a genetic algorithm was proposed to optimize the number of the nodes in the hidden layer, the weights and the thresholds of the traditional neural network by considering the shortcomings of the traditional neural networks which converged slowly and was easy to fall into local minima. Subsequently, the grey cluster grouping and statistical correlation analysis were proposed to group temperature variables and select thermal sensitive points. Then, the thermal error models

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Negnevitsky, Michael, and Martin J. Ringrose. "Fuzzy Control of Back-Propagation Training." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 6 (2000): 408–11. http://dx.doi.org/10.20965/jaciii.2000.p0408.

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A fuzzy logic controller for updating training parameters in the error back-propagation algorithm is presented. The controller is based on heuristic rules for speeding up the convergence of training process, incorporating both learning rate and momentum constant changes.

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Kadnár, Milan, Peter Káčer, Marta Harničárová, et al. "Comparison of Linear Regression and Artificial Neural Network Models for the Dimensional Control of the Welded Stamped Steel Arms." Machines 11, no. 3 (2023): 376. http://dx.doi.org/10.3390/machines11030376.

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The production of parts by pressing and subsequent welding is commonly used in the automotive industry. The disadvantage of this method of production is that inaccuracies arising during pressing significantly affect the final dimension of the part. However, this can be corrected by the choice of the technological parameters of the following operation—welding. Suitably designed parameters make it possible to partially eliminate inaccuracies arising during pressing and thus increase the overall applicability of this technology. The paper is focused on the upper arm geometry of a car produced in

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Dilmagambetova, M., and O. Mamyrbayev. "DEVELOPMENT OF THE NEURAL NETWORK FOR SOLVING THE PROBLEM OF SPEECH RECOGNITION." PHYSICO-MATHEMATICAL SERIES 335, no. 1 (2021): 19–25. http://dx.doi.org/10.32014/2021.2224-5294.3.

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The article discusses a method for solving the problem of speech recognition on the example of recognizing individual words of a limited dictionary using a forward propagation neural network trained by the error back propagation method. The goal was to create a neural network model for recognizing the solution of individual words, analyze the training characteristics and behavior of the constructed neural network. Based on the input data and output requirements, a feedback neural network selected. To train the selected neural network model, a back propagation algorithm was chosen. The develope

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Dilmagambetova, M., and O. Mamyrbayev. "DEVELOPMENT OF THE NEURAL NETWORK FOR SOLVING THE PROBLEM OF SPEECH RECOGNITION." PHYSICO-MATHEMATICAL SERIES 335, no. 1 (2021): 19–25. http://dx.doi.org/10.32014/2021.2518-1726.3.

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The article discusses a method for solving the problem of speech recognition on the example of recognizing individual words of a limited dictionary using a forward propagation neural network trained by the error back propagation method. The goal was to create a neural network model for recognizing the solution of individual words, analyze the training characteristics and behavior of the constructed neural network. Based on the input data and output requirements, a feedback neural network selected. To train the selected neural network model, a back propagation algorithm was chosen. The develope

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Kollam, Manoj, and Ajay Joshi. "Earthquake Forecasting Using Optimized Levenberg–marquardt Back-propagation Neural Network." WSEAS TRANSACTIONS ON COMPUTERS 22 (August 3, 2023): 90–97. http://dx.doi.org/10.37394/23205.2023.22.11.

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In this study, an effective earthquake forecasting model is introduced using a hybrid metaheuristic machine learning (ML) algorithm with CUDA-enabled parallel processing. To improve the performance and accuracy of the model, a novel hybrid ML model is developed that utilizes parallel processing. The model consists of a Chaotic Chimp based African Vulture Optimization Algorithm (CCAVO) for feature selection and a Hybrid Levenberg-Marquardt Back-Propagation Neural Network (HLMt-BPNN) for prediction. The proposed model follows a four-step process: preprocessing the raw data to identify seismic in

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Oh, Sang-Hoon. "Improving the Error Back-Propagation Algorithm for Imbalanced Data Sets." International Journal of Contents 8, no. 2 (2012): 7–12. http://dx.doi.org/10.5392/ijoc.2012.8.2.007.

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Li-Min Du, Zi-Qiang Hou, and Qi-Hu Li. "Optimum block-adaptive learning algorithm for error back-propagation networks." IEEE Transactions on Signal Processing 40, no. 12 (1992): 3032–42. http://dx.doi.org/10.1109/78.175746.

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Khalil K. Abbo, Hassan H. Abrahim, and Firdos A. Abrahim. "A new learning rate based on Andrei method for training feed-forward artificial neural networks." Tikrit Journal of Pure Science 22, no. 2 (2023): 109–12. http://dx.doi.org/10.25130/tjps.v22i2.635.

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In this paper we developed a new method for computing learning rate for Back-propagation algorithm to train a feed-forward neural networks. Our idea is based on the approximating the inverse Hessian matrix for the error function originally suggested by Andrie. Experimental results show that the proposed method considerably improve the convergence rate of the Back-propagation algorithm for the chosen test problem.

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Chen, Yao, Wei Wang, and Ning Li. "Prediction of the equilibrium moisture content and specific gravity of thermally modified wood via an Aquila optimization algorithm back-propagation neural network model." BioResources 17, no. 3 (2022): 4816–36. http://dx.doi.org/10.15376/biores.17.3.4816-4836.

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The equilibrium moisture content and specific gravity of Uludag fir (Abies bornmüelleriana Mattf.) and hornbeam (Carpinus betulus L.) woods were investigated following heat treatment at different temperatures and times. Two prediction models were established based on the Aquila optimization algorithm back-propagation neural network model. To demonstrate the effectiveness and accuracy of the proposed model, it was compared with a tent sparrow search algorithm-back-propagation network model, a back-propagation network model, and an artificial neural network. The results showed that the Aquila op

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Zhao, Qiuhong, Feng Ye, and Shouyang Wang. "A New Back-Propagation Neural Network Algorithm for a Big Data Environment Based on Punishing Characterized Active Learning Strategy." International Journal of Knowledge and Systems Science 4, no. 4 (2013): 32–45. http://dx.doi.org/10.4018/ijkss.2013100103.

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This paper introduces the active learning strategy to the classical back-propagation neural network algorithm and proposes punishing-characterized active learning Back-Propagation (BP) Algorithm (PCAL-BP) to adapt to big data conditions. The PCAL-BP algorithm selects samples and punishments based on the absolute value of the prediction error to improve the efficiency of learning complex data. This approach involves reducing learning time and provides high precision. Numerical analysis shows that the PCAL-BP algorithm is superior to the classical BP neural network algorithm in both learning eff

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Jianhong, Zhu, Pan Wen-xia, and Zhang Zhi-ping. "Embedded Applications of MS-PSO-BP on Wind/Storage Power Forecasting." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 4 (2017): 1610–24. https://doi.org/10.12928/TELKOMNIKA.v15i4.6720.

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Higher proportion wind power penetration has great impact on grid operation and dispatching, intelligent hybrid algorithm is proposed to cope with inaccurate schedule forecast. Firstly, hybrid algorithm of MS-PSO-BP (Mathematical Statistics, Particle Swarm Optimization, Back Propagation neural network) is proposed to improve the wind power system prediction accuracy. MS is used to optimize artificial neural network training sample, PSO-BP (particle swarm combined with back propagation neural network) is employed on prediction error dynamic revision. From the angle of root mean square error (RM

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Xiao, Qi, Rui Wang, Shujie Zhang, Danyang Li, Hongyu Sun, and Limin Wang. "Prediction of pilling of polyester–cotton blended woven fabric using artificial neural network models." Journal of Engineered Fibers and Fabrics 15 (January 2020): 155892501990015. http://dx.doi.org/10.1177/1558925019900152.

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In this article, an intelligent pilling prediction model using back-propagation neural network model and an optimized model with genetic algorithm is introduced. Genetic algorithm is proposed in consideration of the initial weight and threshold of back-propagation artificial neural network, and further improves training speed and the accuracy for prediction pilling of polyester–cotton blended woven fabrics. The results show that the maximum numbers of training steps of the optimized model by genetic algorithm are reduced from 164 steps to 137 steps compared with that of back-propagation model.

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Liang, Siyuan, Tianyu Guo, Rongrong Chen, and Xuguang Li. "Hybrid Filtering Compensation Algorithm for Suppressing Random Errors in MEMS Arrays." Micromachines 15, no. 5 (2024): 558. http://dx.doi.org/10.3390/mi15050558.

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To solve the high error phenomenon of microelectromechanical systems (MEMS) due to their poor signal-to-noise ratio, this paper proposes an online compensation algorithm wavelet threshold back-propagation neural network (WT-BPNN), based on a neural network and designed to effectively suppress the random error of MEMS arrays. The algorithm denoises MEMS and compensates for the error using a back propagation neural network (BPNN). To verify the feasibility of the proposed algorithm, we deployed it in a ZYNQ-based MEMS array hardware. The experimental results showed that the zero-bias instability

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Panda, Saroj Kumar, Papia Ray, Debani Prasad Mishra, and Surender Reddy Salkuti. "Short-term load forecasting of the distribution system using cuckoo search algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 1 (2022): 159. http://dx.doi.org/10.11591/ijpeds.v13.i1.pp159-166.

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For solving the different optimization problems, the cuckoo search is one of the best nature's inspired algorithms. It is an effective technique compare to other optimization methods. For this manuscript, we are using a back propagation neural network for the Xintai power plant consist of short-term electrical load forecasting. The limitation of back propagation is overcome by the cuckoo search algorithm. The function is used for cuckoo search is Gamma probability distribution and its result is compared with other possible cuckoo search methods. The mean average percentage error of Gamma cucko

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Saroj, Kumar Panda, Ray Papia, Prasad Mishra Debani, and Reddy Salkuti Surender. "Short-term load forecasting of the distribution system using cuckoo search algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 1 (2022): 159–66. https://doi.org/10.11591/ijpeds.v13.i1.pp159-166.

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For solving the different optimization problems, the cuckoo search is one of the best nature's inspired algorithms. It is an effective technique compare to other optimization methods. For this manuscript, we are using a back propagation neural network for the Xintai power plant consist of short-term electrical load forecasting. The limitation of back propagation is overcome by the cuckoo search algorithm. The function is used for cuckoo search is Gamma probability distribution and its result is compared with other possible cuckoo search methods. The mean average percentage error of Gamma c

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Inggih, Permana, agustina Ria, Purnamasari Endah, and Nur Salisah Febi. "A Model to Predict The Live Bodyweight of Livestock Using Back-propagation Algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1667–72. https://doi.org/10.12928/TELKOMNIKA.v16i4.6716.

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Cattle is the most popular livestock in Indonesia. Assessments of the live bodyweight of cattle can be conducted through weighing or predicting. Weighing is an accurate method, but it is not efficient due to the prices of scales that most traditional farmers cannot afford. Prediction is a more affordable technique however occurrences of error remains high. To deal with this issue this research has created a model predicting the live bodyweight of cattle through Back-Propagation algorithm. There are four morphometric variables examined in this study: (1) body length; (2) withers height; (3) che

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Qiu, K. Y., H. Huang, and A. El-Rabbany. "GEOMAGNETIC FIELD-BASED INDOOR POSITIONING USING BACK-PROPAGATION NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 557–63. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-557-2020.

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Abstract. High-precision indoor positioning in complex environments has always been a hot research topic within the positioning and robotic communities. As one of the indoor positioning technologies, geomagnetic positioning is receiving widespread attention due to its global coverage. Additionally, geomagnetic positioning does not require special infrastructure configuration, its hardware cost is low, and its positioning errors do not accumulate over time. However, geomagnetic positioning is prone to mismatching, which causes serious problems at the positioning points. To tackle this challenge

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Ni, Bo, Li Li, Hanjie Lin, et al. "Debris flow volume prediction model based on back propagation neural network optimized by improved whale optimization algorithm." PLOS ONE 19, no. 4 (2024): e0297380. http://dx.doi.org/10.1371/journal.pone.0297380.

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Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then,

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Ayyildiz, Mustafa. "Modeling for prediction of surface roughness in milling medium density fiberboard with a parallel robot." Sensor Review 39, no. 5 (2019): 716–23. http://dx.doi.org/10.1108/sr-02-2019-0051.

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Purpose This paper aims to discuss the utilization of artificial neural networks (ANNs) and multiple regression method for estimating surface roughness in milling medium density fiberboard (MDF) material with a parallel robot. Design/methodology/approach In ANN modeling, performance parameters such as root mean square error, mean error percentage, mean square error and correlation coefficients (R2) for the experimental data were determined based on conjugate gradient back propagation, Levenberg–Marquardt (LM), resilient back propagation, scaled conjugate gradient and quasi-Newton back propagat

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Bang, Gul-Won, Dea-Wook Kang, and Wan-Hyun Cho. "Traffic Sign Recognition Using Color Information and Error Back Propagation Algorithm." KIPS Transactions:PartD 14D, no. 7 (2007): 809–18. http://dx.doi.org/10.3745/kipstd.2007.14-d.7.809.

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Kanda, Arihiro, Satoshi Fujita, and Tadashi Ae. "Acceleration by prediction for error back-propagation algorithm of neural network." Systems and Computers in Japan 25, no. 1 (1994): 78–87. http://dx.doi.org/10.1002/scj.4690250107.

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Yan, Jinshun. "3D printing optimization algorithm based on back-propagation neural network." Journal of Engineering, Design and Technology 18, no. 5 (2020): 1223–30. http://dx.doi.org/10.1108/jedt-12-2019-0342.

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Purpose To obtain a high-quality finished product model, three-dimensional (3D) printing needs to be optimized. Design/methodology/approach Based on back-propagation neural network (BPNN), the particle swarm optimization (PSO) algorithm was improved for optimizing the parameters of BPNN, and then the model precision was predicted with the improved PSO-BPNN (IPSO-BPNN) taking nozzle temperature, etc. as the influencing factors. Findings It was found from the experimental results that the prediction results of IPSO-BPNN were closer to the actual values than BPNN and PSO-BPNN, and the prediction

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Al Aboodi, Ali H. "PREDICTION OF TIGRIS RIVER DISCHARGE IN BAGHDAD CITY USING ARTIFICAL NEURAL NETWORKS." Kufa Journal of Engineering 5, no. 2 (2014): 107–16. http://dx.doi.org/10.30572/2018/kje/521325.

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Â Artificial Neural Networks (ANNs), with three layers feed- forward network of sigmoid hidden neurons and linear output neurons are performed for predicting Tigris River flow in Baghdad City, middle of Iraq. The network is trained with Levenberg-Marquradt back-propagation algorithm. The number of hidden neurons is estimated according to trial and error procedure. The best model is selected according to trial and error procedure based on root mean square error and coefficient of correlation. The selected model is used to predicate the river discharge for one, two, and three months ahead. Resul

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Zhou, Mengjie, and Ling Yin. "Research on Thermal Error Modeling Method of Machine Tool Spindle Based on Optimized BP Neural Network." Journal of Physics: Conference Series 2694, no. 1 (2024): 012069. http://dx.doi.org/10.1088/1742-6596/2694/1/012069.

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Abstract Addressing the limitations of the single-temperature measurement point monitoring for detecting the temperature changes in the CNC machine tool spindle, and the shortcomings of the thermal error model based on back propagation neural network (BP) in accuracy, convergence and robustness. This paper studies the thermal error identification model and method of spindle based on multiple temperature sensors. An Adaptive particle swarm algorithm-back-propagation neural network (IAPSO-BP) model for thermal error identification of principal axes is proposed. To enhance modeling accuracy and c

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Gui, X., M. Fečkan, and J. R. Wang. "The application of PSO-BP combined model and GA-BP combined model in Chinese and V4’s economic growth model." Journal of Applied Mathematics, Statistics and Informatics 18, no. 2 (2022): 33–56. http://dx.doi.org/10.2478/jamsi-2022-0011.

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Abstract This paper adopts different optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO-Algorithm) to train Back-Propagation (BP) neural networks, fits the Chinese, the Czech, Slovak, Hungarian, and Polish gross domestic product (GDP) growth model (from 1995 to 2020) and makes short-term simulation predictions. We use the PSO-Algorithm and GA with strong global search ability to optimize the weights and thresholds of the network, combine them with the BP neural network, and apply the resulting Particle Swarm Optimization Back-Propagation (PSO-

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Damper, R. I. "Parity is not a generalisation problem." Behavioral and Brain Sciences 20, no. 1 (1997): 69–70. http://dx.doi.org/10.1017/s0140525x97250028.

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Uninformed learning mechanisms will not discover “type- 2” regularities in their inputs, except fortuitously. Clark & Thornton argue that error back-propagation only learns the classical parity problem – which is “always pure type-2” – because of restrictive assumptions implicit in the learning algorithm and network employed. Empirical analysis showing that back-propagation fails to generalise on the parity problem is cited to support their position. The reason for failure, however, is that generalisation is simply not a relevant issue. Nothing can be gleaned about back-propagation in part

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Xiao, Hui Hui, and Yan Ming Duan. "Application of the Bat Algorithm to Optimize the BP Neural Network." Applied Mechanics and Materials 721 (December 2014): 531–34. http://dx.doi.org/10.4028/www.scientific.net/amm.721.531.

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For the standard BP algorithm usually has the limitations of slow convergence and local extreme values, a new method to adjust weights of BP network was proposed based on the bat algorithm of the global optimization ability and the strong convergence. The new algorithm was based on the weight adjustments of error back propagation of BP algorithm and the weight and threshold of BP network modification using the bats position update. The new algorithm can not only use the bat ability of global optimization, but also contain the feature of error back propagation of BP algorithm. The new algorithm

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Ayyıldız, Mustafa, and Kerim Çetinkaya. "Predictive modeling of geometric shapes of different objects using image processing and an artificial neural network." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 231, no. 6 (2016): 1206–16. http://dx.doi.org/10.1177/0954408916659310.

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In this study, an artificial neural network model was developed to predict the geometric shapes of different objects using image processing. These objects with various sizes and shapes (circle, square, triangle, and rectangle) were used for the experimental process. In order to extract the features of these geometric shapes, morphological features, including the area, perimeter, compactness, elongation, rectangularity, and roundness, were applied. For the artificial neural network modeling, the standard back-propagation algorithm was found to be the optimum choice for training the model. In th

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Nawi, Nazri Mohd, Abdullah Khan, M. Z. Rehman, Haruna Chiroma, and Tutut Herawan. "Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/868375.

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Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposes a new metaheuristic search algorithm called Cuckoo Search (CS) based on Cuckoo bird’s

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Shen, Xingdong, Cui Zhou, and Jianjun Zhu. "Improving the Accuracy of TanDEM-X Digital Elevation Model Using Least Squares Collocation Method." Remote Sensing 15, no. 14 (2023): 3695. http://dx.doi.org/10.3390/rs15143695.

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The TanDEM-X Digital Elevation Model (DEM) is limited by the radar side-view imaging mode, which still has gaps and anomalies that directly affect the application potential of the data. Many methods have been used to improve the accuracy of TanDEM-X DEM, but these algorithms primarily focus on eliminating systematic errors trending over a large area in the DEM, rather than random errors. Therefore, this paper presents the least-squares collocation-based error correction algorithm (LSC-TXC) for TanDEM-X DEM, which effectively eliminates both systematic and random errors, to enhance the accuracy

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Hussien, Elsadek, and Zahraa Elsayed. "Improving Error Back Propagation Algorithm by using Cross Entropy Error Function and Adaptive Learning Rate." International Journal of Computer Applications 161, no. 8 (2017): 5–9. http://dx.doi.org/10.5120/ijca2017913242.

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Oh, Sang-Hoon Oh, and Youngjik Lee Lee. "A Modified Error Function to Improve the Error Back-Propagation Algorithm for Multi-Layer Perceptrons." ETRI Journal 17, no. 1 (1995): 11–22. http://dx.doi.org/10.4218/etrij.95.0195.0012.

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Nasien, Dewi, Veren Enjeslina, M. Hasmil Adiya, and Zirawani Baharum. "Breast Cancer Prediction Using Artificial Neural Networks Back Propagation Method." Journal of Physics: Conference Series 2319, no. 1 (2022): 012025. http://dx.doi.org/10.1088/1742-6596/2319/1/012025.

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Abstract Research on breast cancer has been widely conducted and previously studied with various methods or algorithms to categorize it into benign and malignant groups. In ANN algorithm, one method called back propagation network is utilized to solve complex problems related to identification, pattern recognition prediction, and so forth. The objective of the present study is to investigate the level of accuracy and performance by ANN back propagation in predicting breast cancer. Several stages for this study are formulating the problem, collecting and processing the Wisconsin breast cancer d

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Lee, Hahn-Ming, Chih-Ming Chen, and Tzong-Ching Huang. "Learning efficiency improvement of back-propagation algorithm by error saturation prevention method." Neurocomputing 41, no. 1-4 (2001): 125–43. http://dx.doi.org/10.1016/s0925-2312(00)00352-0.

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Wu, Yijun, and Yonghong Qin. "Machine translation of English speech: Comparison of multiple algorithms." Journal of Intelligent Systems 31, no. 1 (2022): 159–67. http://dx.doi.org/10.1515/jisys-2022-0005.

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Abstract In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neura

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Srivastava, Shikha. "Effect on Neural Pattern Classifier for Intelligent Gas Sensor by Increasing Number of Hidden Layer." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1376–83. http://dx.doi.org/10.22214/ijraset.2021.37583.

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Abstract: Neural networks are used to solve complex problem viz., speech and image recognition, pattern recognition (Pattern classification), computer vision etc. Pattern classification by using Back Propagation algorithm for an intelligent gas sensor application is presented. The classifier is trained using published data of thick film tin oxide sensor array. Its superior classification and learning performance is demonstrated for discrimination of alcohols and alcoholic beverages by increasing number of hidden layer. The new model proposed in this article give steep and monotone learning cur

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Samantaray, Sandeep, and Abinash Sahoo. "Prediction of runoff using BPNN, FFBPNN, CFBPNN algorithm in arid watershed: A case study." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 3 (2020): 243–51. http://dx.doi.org/10.3233/kes-200046.

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Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfe

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Nithyalakshmi, V., Dr R. Sivakumar, and Dr A. Sivaramakrishnan. "Automatic Detection and Classification of Diabetes Using Artificial Intelligence." International Academic Journal of Innovative Research 8, no. 1 (2021): 01–05. http://dx.doi.org/10.9756/iajir/v8i1/iajir0801.

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Diabetes is characterized as a chronic disease that may cause many health complications. Artificial intelligence techniques are adopted diagnose diabetes more accurately. This paper presents an artificial intelligence technique for diabetes diagnosis. Efficacy of the technique is evaluated using diabetes database. Experimental results show that the back propagation neural network algorithm yields the highest classification rate compared to k-nearest neighbourhood classifier. Additionally, the back propagation neural network provides error with the highest area under curve of 90 %.

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Han, Bao Ru, and Jing Bing Li. "Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm." Applied Mechanics and Materials 373-375 (August 2013): 1049–52. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1049.

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Base on improved particle swarm algorithm, this paper proposes a linear decreasing inertia weight particle swarm algorithm and error back propagation algorithm based on hybrid algorithm combining. The linear decreasing inertia weight particle swarm algorithm and momentum-adaptive learning rate BP algorithm interchangeably adjust the network weights, so that the two algorithms are complementary. It gives full play to the PSO's global optimization ability and the BP algorithm local search advantage, to overcome the slow convergence speed and easily falling into local weight problems. Simulation

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Li, Mengci, Zhenbin Gao, Bo Qiu, et al. "Photometric redshifts estimation for galaxies by using FOABP-RF." Monthly Notices of the Royal Astronomical Society 506, no. 4 (2021): 5923–34. http://dx.doi.org/10.1093/mnras/stab2040.

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ABSTRACT This paper proposes a new combinatorial algorithm (FOABP-RF)-using Fruit Fly Optimization Algorithm to enhance Back Propagation Neural Network (FOABP) and random forest (RF) to estimate photometric redshifts of galaxies. This method can improve the estimation accuracy and effectively overcome the shortcomings of artificial neural network which often falls into the local optimal point. And it is suitable for different types of galaxies. First, self-organizing feature mapping (SOM) is used to cluster samples into early-type and late-type galaxies. Then the Back Propagation neural networ

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Lin, Yunhan, Wenlong Ji, Haowei He, and Yaojie Chen. "Two-Stage Water Jet Landing Point Prediction Model for Intelligent Water Shooting Robot." Sensors 21, no. 8 (2021): 2704. http://dx.doi.org/10.3390/s21082704.

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In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fi

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Li, Shuo, Song Li, Haifeng Zhao, and Yuan An. "Design and implementation of state-of-charge estimation based on back-propagation neural network for smart uninterruptible power system." International Journal of Distributed Sensor Networks 15, no. 12 (2019): 155014771989452. http://dx.doi.org/10.1177/1550147719894526.

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In this article, a method for estimating the state of charge of lithium battery based on back-propagation neural network is proposed and implemented for uninterruptible power system. First, back-propagation neural network model is established with voltage, temperature, and charge–discharge current as input parameters, and state of charge of lithium battery as output parameter. Then, the back-propagation neural network is trained by Levenberg–Marquardt algorithm and gradient descent method; and the state of charge of batteries in uninterruptible power system is estimated by the trained back-pro

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