Artykuły w czasopismach: „Back propagation”

1

Chahar, Vikas. "Analysis of Back Propagation Algorithm". International Journal of Scientific Research 2, nr 8 (1.06.2012): 305–6. http://dx.doi.org/10.15373/22778179/aug2013/98.

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A, Dr Deepa, i Fathima Thasliya P A. "Back Propagation". International Journal for Research in Applied Science and Engineering Technology 11, nr 4 (30.04.2023): 334–39. http://dx.doi.org/10.22214/ijraset.2023.50077.

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Abstract: Back Propagation Algorithm research is now very active in the Artificial Neural Network (ANN) and machine learning communities. It has increased a wide range of applications, including image compression, pattern recognition, time series prediction, sequence identification, data filtering, and other intelligent processes carried out by the human brain, have had enormous results. In this paper, we give a quick introduction to ANN and BP algorithms, explain how they operate, and highlight some of the ongoing research projects and the difficulties they face

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Hui, Hui, Dayou Liu i Yafei Wang. "Sequential back-propagation". Journal of Computer Science and Technology 9, nr 3 (lipiec 1994): 252–60. http://dx.doi.org/10.1007/bf02939506.

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Shing Wang, Chau. "Power Disturbance Recognition Using Back-Propagation Neural Networks". International Journal of Engineering and Technology 4, nr 4 (2012): 430–33. http://dx.doi.org/10.7763/ijet.2012.v4.403.

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Bernard, C., i D. Johnston. "Distance-Dependent Modifiable Threshold for Action Potential Back-Propagation in Hippocampal Dendrites". Journal of Neurophysiology 90, nr 3 (wrzesień 2003): 1807–16. http://dx.doi.org/10.1152/jn.00286.2003.

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In hippocampal CA1 pyramidal neurons, action potentials generated in the axon back-propagate in a decremental fashion into the dendritic tree where they affect synaptic integration and synaptic plasticity. The amplitude of back-propagating action potentials (b-APs) is controlled by various biological factors, including membrane potential ( Vm). We report that, at any dendritic location ( x), the transition from weak (small-amplitude b-APs) to strong (large-amplitude b-APs) back-propagation occurs when Vm crosses a threshold potential, θ x. When Vm > θ x, back-propagation is strong (mostly active). Conversely, when Vm < θ x, back-propagation is weak (mostly passive). θ x varies linearly with the distance ( x) from the soma. Close to the soma, θ x ≪ resting membrane potential (RMP) and a strong hyperpolarization of the membrane is necessary to switch back-propagation from strong to weak. In the distal dendrites, θ x ≫ RMP and a strong depolarization is necessary to switch back-propagation from weak to strong. At ∼260 μm from the soma, θ260 ≈ RMP, suggesting that in this dendritic region back-propagation starts to switch from strong to weak. θ x depends on the availability or state of Na+ and K+ channels. Partial blockade or phosphorylation of K+ channels decreases θ x and thereby increases the portion of the dendritic tree experiencing strong back-propagation. Partial blockade or inactivation of Na+ channels has the opposite effect. We conclude that θ x is a parameter that captures the onset of the transition from weak to strong back-propagation. Its modification may alter dendritic function under physiological and pathological conditions by changing how far large action potentials back-propagate in the dendritic tree.

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Buscema, Massimo. "Back Propagation Neural Networks". Substance Use & Misuse 33, nr 2 (styczeń 1998): 233–70. http://dx.doi.org/10.3109/10826089809115863.

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Solanki, Shital. "A review on back propagation algorithms for Feedforward Networks". Global Journal For Research Analysis 2, nr 1 (15.06.2012): 73–75. http://dx.doi.org/10.15373/22778160/january2013/61.

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Garkani-Nejad, Zahra, i Behzad Ahmadi-Roudi. "Investigating the role of weight update functions in developing artificial neural network modeling of retention times of furan and phenol derivatives". Canadian Journal of Chemistry 91, nr 4 (kwiecień 2013): 255–62. http://dx.doi.org/10.1139/cjc-2012-0372.

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A quantitative structure−retention relationship study has been carried out on the retention times of 63 furan and phenol derivatives using artificial neural networks (ANNs). First, a large number of descriptors were calculated using HyperChem, Mopac, and Dragon softwares. Then, a suitable number of these descriptors were selected using a multiple linear regression technique. This paper focuses on investigating the role of weight update functions in developing ANNs. Therefore, selected descriptors were used as inputs for ANNs with six different weight update functions including the Levenberg−Marquardt back-propagation network, scaled conjugate gradient back-propagation network, conjugate gradient back-propagation with Powell−Beale restarts network, one-step secant back-propagation network, resilient back-propagation network, and gradient descent with momentum back-propagation network. Comparison of the results indicates that the Levenberg−Marquardt back-propagation network has better predictive power than the other methods.

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AL-Assady, Nidhal, Baydaa Khaleel i Shahbaa Khaleel. "Improvement the Back-propagation Technique". AL-Rafidain Journal of Computer Sciences and Mathematics 1, nr 2 (1.12.2004): 127–51. http://dx.doi.org/10.33899/csmj.2004.164115.

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Moody, John, i Chris Darken. "Speedy alternatives to back propagation". Neural Networks 1 (styczeń 1988): 202. http://dx.doi.org/10.1016/0893-6080(88)90239-0.

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Šíma, Jiří. "Back-propagation is not Efficient". Neural Networks 9, nr 6 (sierpień 1996): 1017–23. http://dx.doi.org/10.1016/0893-6080(95)00135-2.

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Carmesin, H. O. "Multilinear back-propagation convergence theorem". Physics Letters A 188, nr 1 (maj 1994): 27–31. http://dx.doi.org/10.1016/0375-9601(94)90112-0.

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Toutain, P. L., P. G. Marnet, M. P. Laurentie, R. Garcia-Villar i Y. Ruckebusch. "Direction of uterine contractions during estrus in ewes: a reevaluation". American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 249, nr 4 (1.10.1985): R410—R416. http://dx.doi.org/10.1152/ajpregu.1985.249.4.r410.

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In four ewes direction of propagation of uterine contractions was evaluated using an electromyographic technique during 15 entire estrous periods; 120,101 propagations were analyzed. When horns were considered separately 89.9% of all propagations were classified into three main modes: ascending (34.3%), descending (59.85%), and divergent (5.85%). When both horns were analyzed simultaneously, horn's synchronicity was observed in most instances; on this basis, eight modes of propagation were identified of which three accounted for two-thirds of all propagation: synchronous descending (24.9%), reciprocal propagation (descending on one horn and then ascending on contralateral horn) (22.1%), and isolated descending propagation (18.0%). A time-dependent pattern of propagation throughout estrus was clearly identified, the percentage of ascending propagations reaching a minimum (16.5%) and the percentage of descending propagation reaching a maximum (77%) at peak uterine motility level. By considering both direction of uterine propagation and cervical mechanical activity, a new hypothesis concerning two aspects of sperm transport (speed and mechanism) was formulated. It is hypothesized that the high prevalence of descending propagations is important to reduce sperm cell population by selecting the most vigorous spermatozoa; such selection is possible when the mechanical cervical activity is low or absent (cervix open); when cervical mechanical activity is high (cervix closed), it is suggested that both descending and ascending propagations participate in sperm transport by back and forth motion of luminal fluid within the uterine lumen.

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Demeyer, Séverine, Samuel K. Kristoffersen, Alexis Le Pichon, Franck Larsonnier i Nicolas Fischer. "Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems". Remote Sensing 15, nr 7 (31.03.2023): 1892. http://dx.doi.org/10.3390/rs15071892.

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To improve the confidence and quality of measurements produced by regional and international infrasound monitoring networks, this work investigates a methodology for propagating uncertainty associated with on-site measurement systems. We focus on the propagation of sensor calibration uncertainties. The proposed approach is applied to synthetic infrasound signals with known back azimuth and trace velocity, recorded at the array elements. Relevant input uncertainties are investigated for propagation targeting the incoming signals (noise), instrumentation (microbarometers, calibration system, wind noise reduction system), and the time-delay-of-arrival (TDOA) model (frequency band). Uncertainty propagation is performed using the Monte Carlo method to obtain the corresponding uncertainties of the relevant output quantities of interest, namely back azimuth and trace velocity. The results indicate that, at high frequencies, large sensor uncertainties are acceptable. However, at low frequencies (<0.1 Hz), even a 2∘ sensor phase uncertainty can lead to errors in the back azimuth of up to 5∘ and errors in the trace velocity of 20 m/s.

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Guo, Jiamin, Xiaoxu Zhao, Junhua Guo, Xingfei Yuan, Shilin Dong i Zhixin Xiong. "Model updating of suspended-dome using artificial neural networks". Advances in Structural Engineering 20, nr 11 (1.03.2017): 1727–43. http://dx.doi.org/10.1177/1369433217693629.

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Differences between the practical suspended-dome and the corresponding numerical model are inevitable. To reduce the existing discrepancy, model updating of a suspended-dome was investigated using the back-propagation network in the article. The article first proposed a method to increase the prediction precision of back-propagation network: reducing the range of the training data for the back-propagation network according to the previous prediction results continuously. Then, some parameters that can be measured are updated by the corresponding measured values directly, and other parameters that cannot be directly measured are updated by the corresponding prediction values from back-propagation network. The results indicate that the updated model can predict the experimental model perfectly, and back-propagation network is effective and accurate to predict the given parameters that cannot be described by an algorithm. The results also confirm that the proposed method to increase the prediction precision of back-propagation network is valid.

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Ma, Wei, Rongqi Wang, Xiaoqin Zhou i Xuefan Xie. "The finite element analysis–based simulation and artificial neural network–based prediction for milling processes of aluminum alloy 7050". Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 235, nr 1-2 (1.07.2020): 265–77. http://dx.doi.org/10.1177/0954405420932442.

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The cutting forces will generally suffer massive complex factors, such as material deformation, tool eccentricity and system vibration, which will inevitably induce many great difficulties in accurately modeling the cutting force predictions that are very significant to investigate cutting processes. Therefore, the genetic algorithm optimized back-propagation and particle swarm optimization neural networks will be adopted to effectively construct cutting force prediction models. In these two back-propagation prediction models, the main milling parameters will be defined into their input vectors, and the transient milling forces along three different directions will be selected as their output vectors, then the implicit relationships between input and output vectors can be directly generated through practically training and learning these two built back-propagation models with a set of experimental milling force data. Meanwhile, the finite element analysis method will be also used to predict milling forces through programming two easy-to-operate plug-ins that can efficiently construct finite element analysis models, conveniently define processing parameters, and automatically perform mesh generation. Subsequently, the milling forces predicted by the established genetic algorithm optimized back-propagation and particle swarm optimization back-propagation models will be analytically compared with finite element analysis simulations and experiments; also the stress distribution and chip formations of finite element analysis and experiments will be comparatively investigated. Finally, the obtained results clearly indicate that these two back-propagation models built by artificial neural networks can well agree with finite element analysis simulations and experiments, but the particle swarm optimization back-propagation model is superior to the genetic algorithm optimized back-propagation model, which clearly demonstrate the particle swarm optimization back-propagation model has higher efficiencies and accuracies in predicting the average and transient cutting forces for different milling processes on aluminum alloy 7050.

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Liu, Hai Ying, Ya Qin An, Xia Yang i Kai Yin. "Intelligent Vehicle Coordination Model Based on Back-Propagation Network". Advanced Materials Research 546-547 (lipiec 2012): 84–88. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.84.

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This thesis firstly introduces Back-Propagation Network, the model for the theory and the common process. And we view from two perspectives: the running hour of vehicles and passenger distributed capacity, and adopt Back-Propagation Network into vehicle coordination. We prove that coordination based on Back-Propagation Network is correct through examples.

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Li, Shuo, Song Li, Haifeng Zhao i 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, nr 12 (grudzień 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-propagation neural network. Finally, we build a state-of-charge estimation test platform and connect it to host computer by Ethernet. The performance of state-of-charge estimation based on back-propagation neural network is tested by connecting to uninterruptible power system and compared with the ampere-hour counting method and the actual test data. The results show that the state-of-charge estimation based on back-propagation neural network can achieve high accuracy in estimating state of charge of uninterruptible power system and can reduce the error accumulation caused in long-term operation.

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Dudás, László. "Back-propagation neuronháló logikai játék játszó". Fiatal Műszakiak Tudományos Ülésszaka 1. (1998) (1998): 101–4. http://dx.doi.org/10.36243/fmtu-1998.26.

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Ayodele, Taiwo, Shikun Zhou i Rinat Khusainov. "Email Classification Using Back Propagation Technique". International Journal of Intelligent Computing Research 1, nr 1 (1.03.2010): 3–9. http://dx.doi.org/10.20533/ijicr.2042.4655.2010.0001.

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Sahariya, Prerna. "Back Propagation SIFT using Fuzzy Logic". International Journal for Research in Applied Science and Engineering Technology 7, nr 6 (30.06.2019): 1470–75. http://dx.doi.org/10.22214/ijraset.2019.6252.

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Teo, Tat-Jin. "Ultrasound image reconstruction using back-propagation". Journal of the Acoustical Society of America 102, nr 6 (1997): 3255. http://dx.doi.org/10.1121/1.419573.

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Lacher, R. C., S. I. Hruska i D. C. Kuncicky. "Back-propagation learning in expert networks". IEEE Transactions on Neural Networks 3, nr 1 (1992): 62–72. http://dx.doi.org/10.1109/72.105418.

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HERRFELD, A., i S. HENTSCHKE. "A parallel back-propagation adder structure". International Journal of Electronics 85, nr 3 (wrzesień 1998): 273–91. http://dx.doi.org/10.1080/002072198134102.

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Sontag, Eduardo D., i Héctor J. Sussmann. "Back propagation separates where perceptrons do". Neural Networks 4, nr 2 (1991): 243–49. http://dx.doi.org/10.1016/0893-6080(91)90008-s.

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Samad, Tariq. "Back propagation with expected source values". Neural Networks 4, nr 5 (styczeń 1991): 615–18. http://dx.doi.org/10.1016/0893-6080(91)90015-w.

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Denoeux, Thierry, i Régis Lengellé. "Initializing back propagation networks with prototypes". Neural Networks 6, nr 3 (styczeń 1993): 351–63. http://dx.doi.org/10.1016/0893-6080(93)90003-f.

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Negnevitsky, Michael, i Martin J. Ringrose. "Fuzzy Control of Back-Propagation Training". Journal of Advanced Computational Intelligence and Intelligent Informatics 4, nr 6 (20.11.2000): 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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., Bekir Karlik, i Yousif Al-Bastaki . "Materials Matching Using Back-Propagation Algorithm". Information Technology Journal 2, nr 1 (15.12.2002): 69–71. http://dx.doi.org/10.3923/itj.2003.69.71.

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Hirose, A. "Continuous complex-valued back-propagation learning". Electronics Letters 28, nr 20 (1992): 1854. http://dx.doi.org/10.1049/el:19921186.

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Aswini Priyanka, R., C. Ashwitha, R. Arun Chakravarthi i R. Prakash. "Face Recognition Model Using Back Propagation". International Journal of Engineering & Technology 7, nr 3.34 (1.09.2018): 237. http://dx.doi.org/10.14419/ijet.v7i3.34.18973.

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In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.

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Drago, G. P., M. Morando i S. Ridella. "An Adaptive Momentum Back Propagation (AMBP)". Neural Computing & Applications 3, nr 4 (grudzień 1995): 213–21. http://dx.doi.org/10.1007/bf01414646.

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Lachtermacher, Gerson, i J. David Fuller. "Back propagation in time-series forecasting". Journal of Forecasting 14, nr 4 (lipiec 1995): 381–93. http://dx.doi.org/10.1002/for.3980140405.

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Li, Jing Zhao, i Gao Xin Cheng. "Research on Multi-Information Fusion Control System of Textile Workshop Environment". Applied Mechanics and Materials 130-134 (październik 2011): 3803–6. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3803.

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Back propagation network can provide dynamic maps and deal with historical data by increasing self-loop. By using the improved circle back propagation neural network, Multi-information fusion algorithm are realized and used to environment control system of textile workshop. Hardware circuit of the system is designed with the embedded microprocessor and appropriate detection devices. The training and algorithmic design of self-loop back propagation network can be achieved by embedded systems programming based on the portable operating system. Application shows that the multi-information fusion control system of textile workshop Environment, which based on local self-loop back-propagation network, has high control precision.

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V, Gayathri. "An Enhanced and Automatic Skin Cancer Detection Using Back Propagation Neural Network". Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25.07.2020): 1969–74. http://dx.doi.org/10.5373/jardcs/v12sp7/20202312.

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Phadermrod, Boonyarat, Richard M. Crowder i Gary B. Wills. "Attribute Importance Measure Based on Back-Propagation Neural Network: An Empirical Study". International Journal of Computer and Electrical Engineering 7, nr 2 (2015): 118–27. http://dx.doi.org/10.17706/ijcee.2015.v7.878.

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Kim. "Development of Optimum Traffic Safety Evaluation Model Using the Back-Propagation Algorithm". Journal of the Korean Society of Civil Engineers 35, nr 3 (2015): 679. http://dx.doi.org/10.12652/ksce.2015.35.3.0679.

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Chen, Yao, Wei Wang i 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, nr 3 (30.06.2022): 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 optimization algorithm back-propagation model reduced the root mean square error value of the original back-propagation model by 87% and 97%, respectively, and the decision coefficients (R2) of the equilibrium moisture content and specific gravity were 0.99 and 0.98; as such, the model optimization effect was obvious. Therefore, this paper provides an effective method for the optimization of the process parameters (such as heat treatment time, temperature, and air pressure) in wood heat treatment and related fields.

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Zhang, Yu, Jiawen Zhang, Lin Luo i Xiaorong Gao. "Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm". International Journal of Distributed Sensor Networks 15, nr 10 (październik 2019): 155014771988134. http://dx.doi.org/10.1177/1550147719881348.

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It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg–Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg–Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.

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Wu, T. S., H. P. Fu, G. Jin, H. F. Wu i H. M. Bai. "Prediction of the livestock carrying capacity using neural network in the meadow steppe". Rangeland Journal 41, nr 1 (2019): 65. http://dx.doi.org/10.1071/rj18058.

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In order to predict the livestock carrying capacity in meadow steppe, a method using back propagation neural network is proposed based on the meteorological data and the remote-sensing data of Normalised Difference Vegetation Index. In the proposed method, back propagation neural network was first utilised to build a behavioural model to forecast precipitation during the grass-growing season (June–July–August) from 1961 to 2015. Second, the relationship between precipitation and Normalised Difference Vegetation Index during the grass-growing season from 2000 to 2015 was modelled with the help of back propagation neural network. The prediction results demonstrate that the proposed back propagation neural network-based model is effective in the forecast of precipitation and Normalised Difference Vegetation Index. Thus, an accurate prediction of livestock carrying capacity is achieved based on the proposed back propagation neural network-based model. In short, this work can be used to improve the utilisation of grassland and prevent the occurrence of vegetation degradation by overgrazing in drought years for arid and semiarid grasslands.

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Moskvin, G. "Back Propagation and Transformation Methods in Artificial Intelligence Systems". Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (26.06.2006): 367. http://dx.doi.org/10.17770/etr2003vol1.2028.

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Detailed description of methods of back propagation and back transformation also distributions for training of neural networks is given. A comparative estimation of a priority of methods of back transformation and back propagation for the decision of tasks of synthesis and training of neural networks, also for intelligent automatic measuring and AI systems for the first time is carried out.

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Ling, Guobi, Zhiwen Wang, Yaoke Shi, Jieying Wang, Yanrong Lu i Long Li. "Membrane Fouling Prediction Based on Tent-SSA-BP". Membranes 12, nr 7 (4.07.2022): 691. http://dx.doi.org/10.3390/membranes12070691.

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In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.

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Yacim, Joseph Awoamim, i Douw Gert Brand Boshoff. "Combining BP with PSO algorithms in weights optimisation and ANNs training for mass appraisal of properties". International Journal of Housing Markets and Analysis 11, nr 2 (3.04.2018): 290–314. http://dx.doi.org/10.1108/ijhma-02-2017-0021.

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Purpose The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models. Design/methodology/approach The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics. Findings The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes. Research limitations/implications A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values. Originality/value This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

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Miao, Zhi Nong, i Hui Jun Zheng. "An Improved Back-Propagation Algorithm for Fuzzy Modeling". Applied Mechanics and Materials 48-49 (luty 2011): 198–202. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.198.

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Fuzzy modeling is discussed in many literatures and there are numerous algorithms are proposed. Back-propagation algorithm is an efficient algorithm for fuzzy modeling and many papers proposed the usage of such method. But there exists potential risk of dead zone, abrupt inference surface and decreasing sensitivity for normal back-propagation algorithm in fuzzy modeling. This paper analysis the potential problems of normal algorithm and suggest a reformative back-propagation algorithm for fuzzy modeling. A complete algorithm is presented in the paper and some simulate result is discussed

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Hana, M., W. F. McClure, T. B. Whitaker, M. White i D. R. Bahler. "Applying Artificial Neural Networks. I. Estimating Nicotine in Tobacco from near Infrared Data". Journal of Near Infrared Spectroscopy 3, nr 3 (czerwiec 1995): 133–42. http://dx.doi.org/10.1255/jnirs.64.

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Two artificial neural network models were used to estimate the nicotine in tobacco: (i) a back-propagation network and (ii) a linear network. The back-propagation network consisted of an input layer, an output layer and one hidden layer. The linear network consisted of an input layer and an output layer. Both networks used the generalised delta rule for learning. Performances of both networks were compared to the multiple linear regression method MLR of calibration. The nicotine content in tobacco samples was estimated for two different data sets. Data set A contained 110 near infrared (NIR) spectra each consisting of reflected energy at eight wavelengths. Data set B consisted of 200 NIR spectra with each spectrum having 840 spectral data points. The Fast Fourier transformation was applied to data set B in order to compress each spectrum into 13 Fourier coefficients. For data set A, the linear regression model gave better results followed by the back-propagation network which was followed by the linear network. The true performance of the linear regression model was better than the back-propagation and the linear networks by 14.0% and 18.1%, respectively. For data set B, the back-propagation network gave the best result followed by MLR and the linear network. Both the linear network and MLR models gave almost the same results. The true performance of the back-propagation network model was better than the MLR and linear network by 35.14%.

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Hua, Chi, Erxi Zhu, Liang Kuang i Dechang Pi. "Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation". International Journal of Distributed Sensor Networks 15, nr 10 (październik 2019): 155014771988313. http://dx.doi.org/10.1177/1550147719883134.

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Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.

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Ding, Shuo, Xiao Heng Chang i Qing Hui Wu. "Application of Probabilistic Neural Network in Pattern Classification". Applied Mechanics and Materials 441 (grudzień 2013): 738–41. http://dx.doi.org/10.4028/www.scientific.net/amm.441.738.

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The network model of probabilistic neural network and its method of pattern classification and discrimination are first introduced in this paper. Then probabilistic neural network and three usually used back propagation neural networks are established through MATLAB7.0. The pattern classification of dots on a two-dimensional plane is taken as an example. Probabilistic neural network and improved back propagation neural networks are used to classify these dots respectively. Their classification results are compared with each other. The simulation results show that compared with back propagation neural networks, probabilistic neural network has simpler learning rules, faster training speed and it needs fewer training samples; the pattern classification method based on probabilistic neural network is very effective, and it is superior to the one based on back propagation neural networks in classifying speed, accuracy as well as generalization ability.

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Kumar, Santosh, Neelappa Neelappa, Saroja Bhusare i Veeramma Yatnalli. "Improving graphics processing unit performance based on neural network direct memory access controller". Indonesian Journal of Electrical Engineering and Computer Science 32, nr 3 (1.12.2023): 1476. http://dx.doi.org/10.11591/ijeecs.v32.i3.pp1476-1484.

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In this paper proposes the design and implementation of the back propagation algorithm based neural network DMA (Direct Memory Access) Controller for use of multimedia applications. The proposed DMA controller work with the back propagation-training algorithm. The advantages of the back propagation algorithm it will be work on the gradient loss w.r.t the network weights. So this back propagation algorithm is used as training algorithm for the DMA controller. The proposed method is test with the different workload characteristics like heavy workload, medium workload and normal workload. The performance parameters are considered here is like accuracy, precision, recall and F1 score etc. The proposed method is compared with existing methods like CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM (Long Sort term Memory) and GRU (Gated Recurrent Unit) etc. Finally, the proposed design will give the better performance than existing methods.

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Damper, R. I. "Parity is not a generalisation problem". Behavioral and Brain Sciences 20, nr 1 (marzec 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 particular, or learning in general, from this failure.

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Murugan, A., i P. J. Arul Leena Rose. "Fingerprint Matching through Back Propagation Neural Network". Indian Journal of Science and Technology 10, nr 29 (1.02.2017): 1–7. http://dx.doi.org/10.17485/ijst/2017/v10i29/93883.

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