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Relevant bibliographies by topics / Back propagation function network

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Academic literature on the topic 'Back propagation function network'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Back propagation function network"

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Asaad, Renas Rajab, and Rasan I. Ali. "Back Propagation Neural Network(BPNN) and Sigmoid Activation Function in Multi-Layer Networks." Academic Journal of Nawroz University 8, no. 4 (2019): 216. http://dx.doi.org/10.25007/ajnu.v8n4a464.

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Back propagation neural network are known for computing the problems that cannot easily be computed (huge datasets analysis or training) in artificial neural networks. The main idea of this paper is to implement XOR logic gate by ANNs using back propagation neural network for back propagation of errors, and sigmoid activation function. This neural network to map non-linear threshold gate. The non-linear used to classify binary inputs (x1, x2) and passing it through hidden layer for computing coefficient_errors and gradient_errors (Cerrors, Gerrors), after computing errors by (ei = Output_desir

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Garkani-Nejad, Zahra, and 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, no. 4 (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−Ma

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Mahmoud, Waleed Ameen, Ali Ibrahim Abbas, and Nuha Abdul Sahib Alwan. "FACE IDENTIFICATION USING BACK-PROPAGATION ADAPTIVE MULTIWAVENET." Journal of Engineering 18, no. 03 (2023): 392–402. http://dx.doi.org/10.31026/j.eng.2012.03.12.

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Face Identification is an important research topic in the field of computer vision and pattern recognition and has become a very active research area in recent decades. Recently multiwavelet-based neural networks (multiwavenets) have been used for function approximation and recognition, but to our best knowledge it has not been used for face Identification. This paper presents a novel approach for the Identification of human faces using Back-Propagation Adaptive Multiwavenet. The proposed multiwavenet has a structure similar to a multilayer perceptron (MLP) neural network with three layers, bu

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Pan, Hao. "An Improved Back-Propagation Neural Network Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 4586–90. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4586.

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Based on the idea of standard back-propagation (BP) learning algorithm, an improved BP learning algorithm is presented. Three parameters are incorporated into each processing unit to enhance the output function. The improved BP learning algorithm is developed for updating the three parameters as well as the connection weights. It not only improves the learning speed, but also reduces the occurrence of local minima. Finally, the algorithm is tested on the XOR problem to verify the validity of the improved BP.

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Tang, Chuan Yin, Guang Yao Zhao, Yi Min Zhang, and Xiao Yu E. "Research on Active Suspension System Based on BP and RBF Network Algorithm." Advanced Materials Research 230-232 (May 2011): 149–53. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.149.

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A six degrees of freedom half body vehicle suspension system is presented in the paper .The Back Propagation neural network algorithm and the Radial-Basis Function network algorithm is adopted to control the suspension system. With the aid of software Matlab/Simulink , the simulation model is obtained. A great deal of simulation work is done. Simulation results demonstrate that both the designed radius basis function neural network and the back propagation neural network work well for the proposed vehicle suspension model in the paper .

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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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Ding, Shuo, Xiao Heng Chang, and Qing Hui Wu. "A Study on Approximation Performances of General Regression Neural Network." Applied Mechanics and Materials 441 (December 2013): 713–16. http://dx.doi.org/10.4028/www.scientific.net/amm.441.713.

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In order to study the approximation performance of general regression neural networks, the structure and algorithm of general regression neural networks are first introduced. Then general regression neural networks and back propagation neural networks improved by Levenberg-Marquardt algorithm are established through programming using MATLAB language. A certain nonlinear function is taken as an example to be approximated by the two kinds of neural networks. The simulation results indicate that compared with back propagation neural networks, general regression neural networks has better approxim

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Morris, A. S., and M. A. Mansor. "Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table." Robotica 16, no. 4 (1998): 433–44. http://dx.doi.org/10.1017/s0263574798001064.

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This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-prop

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Heidari, Mohammad, and Hadi Homaei. "Estimation of Acceleration Amplitude of Vehicle by Back Propagation Neural Networks." Advances in Acoustics and Vibration 2013 (June 4, 2013): 1–7. http://dx.doi.org/10.1155/2013/614025.

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This paper investigates the variation of vertical vibrations of vehicles using a neural network (NN). The NN is a back propagation NN, which is employed to predict the amplitude of acceleration for different road conditions such as concrete, waved stone block paved, and country roads. In this paper, four supervised functions, namely, newff, newcf, newelm, and newfftd, have been used for modeling the vehicle vibrations. The networks have four inputs of velocity (), damping ratio (), natural frequency of vehicle shock absorber (), and road condition (R.C) as the independent variables and one out

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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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Dissertations / Theses on the topic "Back propagation function network"

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Rimer, Michael Edwin. "Improving Neural Network Classification Training." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2094.pdf.

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Aguilar, David P. "A radial basis neural network for the analysis of transportation data." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000515.

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Manoharan, Madhu. "Evaluation of a neural network for formulating a semi-empirical variable kernel BRDF model." Master's thesis, Mississippi State : Mississippi State University, 2005. http://library.msstate.edu/content/templates/?a=72.

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Čermák, Justin. "Implementace umělé neuronové sítě do obvodu FPGA." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219363.

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This master's thesis describes the design of effective working artificial neural network in FPGA Virtex-5 series with the maximum use of the possibility of parallelization. The theoretical part contains basic information on artificial neural networks, FPGA and VHDL. The practical part describes the used format of the variables, creating non-linear function, the principle of calculation the single layers, or the possibility of parameter settings generated artificial neural networks.

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Зимовець, Т. С. "Інтелектуальна інформаційна технологія комп'ютерного діагностування патології волосся". Master's thesis, Сумський державний університет, 2020. https://essuir.sumdu.edu.ua/handle/123456789/78595.

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Проведено синтез системи підтримки прийняття рішень, яка здатна навчатися з використанням нейромережевої технології. Для чого використовувалася нейронна мережа зворотнього поширення. У роботі проведена оптимізація параметрів стандартного алгоритму навчання нейронної мережі такого типу, що дозволило підвищити точність сформованого нейронно мережевого класифікатора. Програмна реалізація виконувалася з використанням пакета розширення NNToolBox середовища MATLAB 6.5.

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Bennett, Richard Campbell. "Classification of underwater signals using a back-propagation neural network." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA331774.

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Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, June 1997.<br>Thesis advisors, Monique P. Fargues, Roberto Cristi. Includes bibliographical references (p. 95). Also available online.

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Le, Chau Giang. "Application of a back-propagation neural network to isolated-word speech recognition." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA272495.

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Rose, Stephen Matthew. "Online training of a neural network controller by improved reinforcement back-propagation." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/19177.

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Tung, Kuo-Feng, and 童國峰. "Application of Agent-Based Back Propagation Neural Network System in Gene Function Association Discovery." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/67898462525911193598.

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碩士<br>國立臺灣大學<br>工業工程學研究所<br>92<br>Software agent has becoming popular in recent years due to its autonomous, mobile, and distributed computation abilities. Agent can negotiate to each other in common platform or in internet. They have been successfully used in distributed applications, information retrieval and intelligent expert systems. Human genomic sequence has just been fully determined as well as other species’ sequences. Many novel genes predicted require functional annotation in order to examine their linkages with diseases. It is essential to determine the functions in these new genes

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Huang, Chun-Hsun, and 黃俊勳. "Application of Membership Function and Back-Propagation Network on Urban Commute-Journey Forecasting Model." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/07291200106593300271.

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碩士<br>中原大學<br>土木工程研究所<br>89<br>The arrangement of daily journeys and activities depends on capability constraints, coupling constraints and authority constraints faced by individuals. In order to optimize the activities out of journeys, travelers tend to chain trips along the way to or from work. In this study, we use trip chains as analysis units and establish urban commute-journey forecasting models based on various methods. By using the regression method, numbers of assumptions need to be satisfied. However, the activity-travel behaviors are often affected by external environment

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Books on the topic "Back propagation function network"

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Bennett, Richard Campbell. Classification of underwater signals using a back-propagation neural network. Naval Postgraduate School, 1997.

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Walker, James L. Back propagation neural networks for predicting ultimate strengths of unidirectional graphite/epoxy tensile specimens. National Aeronautics and Space Administration, 1993.

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Classification of Underwater Signals Using a Back-Propagation Neural Network. Storming Media, 1997.

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Tandon, Neha. Novel Approach for Drug Discovery Using Neural Network Back Propagation Algorithm. GRIN Verlag GmbH, 2018.

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Intelligent information retrieval using an inductive learning algorithm and a back-propagation neural network. University Microfilms International, 1995.

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Book chapters on the topic "Back propagation function network"

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Wu, Teng, Jie Qin, and Runzhuo Guo. "Ecological Evaluation of Waterways Based on Modified Neural Networks." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6138-0_97.

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AbstractThe ecological condition of waterways has attracted increasing public attention in recent years, and the evaluation of the ecological status of waterways is of practical and scientific significance. To carry out an objective and credential evaluation of the ecological condition of waterways, this study compares the performance of two artificial neural networks (ANN) models, including the traditional Back Propagation (BP)-ANN model and the Particle Swarm Optimization (PSO)-BP-ANN model. The traditional BP-ANN model is characterized by a local search strategy and usually converges to loc

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Ngaopitakkul, Atthapol, and Sulee Bunjongjit. "Selection of Proper Activation Functions in Back-Propagation Neural Network Algorithm for Transformer and Transmission System Protection." In Transactions on Engineering Technologies. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9588-3_22.

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Müller, Berndt, and Joachim Reinhardt. "PERBOOL: Learning Boolean Functions with Back-Propagation." In Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3_22.

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Müller, Berndt, and Joachim Reinhardt. "PERFUNC: Learning Continuous Functions with Back-Propagation." In Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3_23.

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Matsuoka, Kiyotoshi. "An Approach to Generalization Problem in Back-Propagation Learning." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_70.

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Chang, Pei-Chann, Yen-Wen Wang, and Chen-Hao Liu. "Fuzzy Back-Propagation Network for PCB Sales Forecasting." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_45.

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Faure, Bernard, and Guy Mazare. "Implementation of Back-Propagation on a VLSI Asynchronous Cellular Architecture." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_28.

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Yang, Wenjun, Bingwen Wang, Zhuo Liu, and Xiaoya Hu. "Back Propagation Neural Network Based Lifetime Analysis of Wireless Sensor Network." In Advances in Neural Networks – ISNN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01513-7_105.

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Chen, D. S., and R. C. Jain. "A robust back-propagation learning algorithm for function approximation." In Artificial Intelligence Frontiers in Statistics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4537-2_17.

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Jokinen, Petri A. "On the Back-Propagation Training of Neural Networks with Noisy Data." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_181.

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Conference papers on the topic "Back propagation function network"

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Kamra, Nidhi, and Alex Ellery. "Forward- And Back-Propagation with an Analog Neural Network." In IAF Space Systems Symposium, Held at the 75th International Astronautical Congress (IAC 2024). International Astronautical Federation (IAF), 2024. https://doi.org/10.52202/078372-0090.

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Ng, S. C., S. H. Leung, A. Luk, and Yunfeng Wu. "Convergence Analysis of Generalized Back-propagation Algorithm with Modified Gradient Function." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247381.

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Mahira, Tomomi, Nevrez Imamoglu, Jose Gomez-Tames, Kahori Kita, and Wenwei Yu. "Modeling bimanual coordination using back propagation neural network and radial basis function network." In 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2014. http://dx.doi.org/10.1109/robio.2014.7090522.

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Wen, Hui, Weixin Xie, and Jihong Pei. "A pre-radical basis function with deep back propagation neural network research." In 2014 12th International Conference on Signal Processing (ICSP 2014). IEEE, 2014. http://dx.doi.org/10.1109/icosp.2014.7015247.

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Kenue, Surender K. "Efficient activation functions for the back-propagation neural network." In Robotics - DL tentative, edited by David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.135110.

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Matuck, Gustavo R., Joa˜o Roberto Barbosa, Cleverson Bringhenti, and Isaias Lima. "Multiple Faults Detection of Gas Turbine by MLP Neural Network." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59964.

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This paper describes a procedure to measure the performance of detection and isolation of multiple faults in gas turbines using artificial neural network and optimization techniques. It is on a particular form of artificial neural networks, the traditional multi-layer perceptron (MLP). Error back-propagation and different activation functions are used. The main goal is to recognize single, double and triple faults in a turboshaft engine, whose performance data were output from a gas turbine simulator program, tuned to represent the engine running at an existing power station. MLP network is a

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Sun, Pengpeng, Yong Liu, Guohua Wu, and Zhiyong Duan. "Research on Fault Diagnosis of Reactor Coolant Accident in Nuclear Power Plant Based on Radial Basis Function and Fuzzy Neural Network." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16138.

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Abstract Nuclear power plants (NPPs) are widely used in the world. After three nuclear accidents, people propose higher of the safety and reliability on NPPs. Reactor coolant system (RCS) in the NPP directly affects whether the heat can be exported and radioactivity can be inclusive. It plays an important role of the NPPs safety. So, it is great significance of fault diagnosis for RCS in NPP. Although many scholar had carried out research on fault diagnosis of NPPs, different networks may lead to different results in a system. Therefore, this paper chooses a system and uses different neural ne

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Perez, Carlos, and Juan De Dios Calderon. "Comparison Between Feed-Forward Back-Propagation and Radial Basis Functions Networks for Roughness Modeling in Face-Milling of Aluminum." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-63364.

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The technology of the cutting process has evolved substantially in terms of materials, tools and machines; however it is a necessity to develop models for control and optimization of the cutting processes because nowadays industry relies mainly on empirical data and heuristic solutions provided by shop-floor experts. Due to the complex relationship between the variables of the cutting process, application of artificial intelligence approaches is wide feasible as modeling technique and functional for controller development. This work, presents a design of experiments, data analysis and model co

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Yoo, J., and P. Hajela. "Optimal Design of Stiffened Composite Panel for Performance and Manufacturing Considerations." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2168.

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Abstract This paper describes a design study in which a stiffened composite wing panel is configured for a combination of performance and manufacturing related requirements. The principal focus of the paper resides in demonstrating the adaptation of newly emergent soft-computing methods for a variety of sub-tasks that constitute the design process. These sub-tasks include function approximations, modeling of processes that lack a good analytical description, and design optimization in a space that consists of a mix of integer, discrete, and continuous design variables. Soft computing technique

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Mukherjee, Indrajit, and Pradip Kumar Ray. "Near Optimal Grinding Process Design Using Neural Network and Real Coded Genetic Algorithm." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95256.

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A typical grinding process is an essential manufacturing operation and has been considered to be a precise and economical means of shaping the parts into the final products with required surface finish and high dimensional accuracy. The need to economically process hard and tough materials which can withstand varying stress conditions to ensure prolonged service life of parts has become a real challenge for researchers and practitioners. In this context, with the advance development and automation of grinding processes, use of appropriate modelling and optimization techniques has been continua

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Reports on the topic "Back propagation function network"

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St. George, Brett A. Speech Coding and Phoneme Classification Using a Back-Propagation Neural Network. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada418472.

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Vanderbeek, Richard G., and Alice M. Harper. Back-Propagation Network for Analog Signal Separation in High Noise Environments. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada254245.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti

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Wilkins, C. A., and W. A. Sands. Comparison of a Back Propagation Artificial Neural Network Model with a Linear Regression Model for Personnel Selection. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada280023.

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Kinnan, Cynthia, Krislert Samphantharak, Robert Townsend, and Diego A. Vera-Cossio. Propagation and Insurance in Village Networks. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004385.

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In village economies, small firm owners facing idiosyncratic shocks adjust production by cutting spending and reducing employment. Households with whom they trade inputs and labor scale back their own businesses and reduce consumption. As effects reverberate through local economies, the aggregate indirect adverse effects are larger than the direct effects. Propagation is more severe when transmitted through labor networks as opposed to material supply-chain networks, and goes beyond input-output/sectoral considerations as it varies with network position, closeness to a shocked household, and n

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Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condit

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Kirichek, Galina, Vladyslav Harkusha, Artur Timenko, and Nataliia Kulykovska. System for detecting network anomalies using a hybrid of an uncontrolled and controlled neural network. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3743.

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In this article realization method of attacks and anomalies detection with the use of training of ordinary and attacking packages, respectively. The method that was used to teach an attack on is a combination of an uncontrollable and controlled neural network. In an uncontrolled network, attacks are classified in smaller categories, taking into account their features and using the self- organized map. To manage clusters, a neural network based on back-propagation method used. We use PyBrain as the main framework for designing, developing and learning perceptron data. This framework has a suffi

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WANG, Peng, Zhidong CAI, Qingying ZHAO, Wanting JIANG, Cong LIU, and Xing WANG. A Bayesian Network Meta-analysis of the Effect of Acute Exercise on Executive Function in Middle-aged and Senior People. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.12.0086.

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Review question / Objective: Objective: To compare the intervention effect of multiple acute movement formulas on the executive function in middle-aged and senior people and to provide references for the discussion of the plans for precise movements. P: middle-aged and senior people elderly people; I: acute exercise; C: reading or sitting; O: Executive Function; S: RCT/crossover. Information sources: Randomized searches were carried out in Chinese databases such as CNKI, Wanfang Database, VTTMS, SinoMed and foreign databases such as PubMed, EMBASE, Cochrane Library, Web of Science. The retriev

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McDonald, Jacob, and M. Gregory. Back barrier erosion monitoring at Cumberland Island National Seashore: 2018 data summary—Version 2.0. National Park Service, 2019. https://doi.org/10.36967/2259083.

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In 1999, the National Park Service’s (NPS) Inventory and Monitoring (I&M) Program (now I&M Division) substantially expanded a pilot long-term ecological monitoring program known as “Vital Signs Monitoring” to cover more than 270 parks. The program was designed to provide the minimum infrastructure required to identify and monitor the conditions of the highest priority resources within the National Park System (Fancy et al. 2009). The Southeast Coast Network (SECN) was one of thirty-two networks formed to implement the I&M Division’s program and is tailored to the specific needs of

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Kwak, Sang Gyu, Yoo Jin Choo, Soyoung Kwak, and Min Cheol Chang. Efficacy of Transforaminal, Interlaminar, and Caudal Epidural Injections in Lumbosacral Disc Herniation: A Systematic Review and Network Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.8.0091.

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Review question / Objective: Epidural injection (EI) has been used to manage lower back or radicular leg pain from herniation of lumbar disc (HLD). Three types of EI techniques, including transforaminal (TFEI) interlaminar (ILEI), and caudal epidural injections (CEI), are being applied. We aimed to evaluate the comparative effect of TFESI, ILEI, and CEI for reducing pain or improving function in patients with HLD. Condition being studied: For controlling inflammation by the HLD, various oral medications and procedures are used. Among these therapeutic methods, EI of the drugs is frequently use

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