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Статті в журналах з теми "BACK PROPAGATION ALGORITHM (BPA)"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

R., Bhuvana, Purushothaman S., Rajeswari R., and Balaji R.G. "Development of combined back propagation algorithm and radial basis function for diagnosing depression patients." International Journal of Engineering & Technology 4, no. 1 (February 27, 2015): 244. http://dx.doi.org/10.14419/ijet.v4i1.4201.

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Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA) and Radial Basis Function (RBF) are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN) and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.

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2

Khanum, Afshan, S. Purushothaman, and P. Rajeswari. "Performance comparisons of the soft computing algorithms in lung segmentation and nodule identification." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 189. http://dx.doi.org/10.14419/ijet.v7i1.1.9287.

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This paper presents the implementation back propagation algorithm (BPA) and fuzzy logic(FL) in lung image segmentation and nodule identification. Lung image database consortium (LIDC) database images has been used. Features are extracted using statistical methods. These features are used for training the BPA and FL algorithms. Weights are stored in a file that is used for segmentation of the lung image. Subsequently, texture properties are used for nodule identification.

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3

Al-Araji, Ahmed Sabah, and Shaymaa Jafe'er Al-Zangana. "Design of New Hybrid Neural Controller for Nonlinear CSTR System based on Identification." Journal of Engineering 25, no. 4 (April 1, 2019): 70–89. http://dx.doi.org/10.31026/j.eng.2019.04.06.

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This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm namely Particle Swarm Optimization (PSO) algorithm. The numerical simulation results show that the hybrid NARMA-L2 controller with PSO algorithm is more accurate than BPA in terms of achieving fast learning and adjusting the parameters model with minimum number of iterations, minimum number of neurons in the hybrid network and the smooth output one step ahead prediction controller response for the nonlinear CSTR system without oscillation.

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4

Sujatha, K., N. Pappa, U. Siddharth Nambi, C. R. Raja Dinakaran, and K. Senthil Kumar. "Intelligent Parallel Networks for Combustion Quality Monitoring in Power Station Boilers." Advanced Materials Research 699 (May 2013): 893–99. http://dx.doi.org/10.4028/www.scientific.net/amr.699.893.

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This research work includes a combination of Fisher’s Linear Discriminant (FLD) analysis by combining Radial Basis Function Network (RBF) and Back Propagation Algorithm (BPA) for monitoring the combustion conditions of a coal fired boiler so as to control the air/fuel ratio. For this two dimensional flame images are required which was captured with CCD camera whose features of the images, average intensity, area, brightness and orientation etc., of the flame are extracted after pre-processing the images. The FLD is applied to reduce the n-dimensional feature size to 2 dimensional feature size for faster learning of the RBF. Also three classes of images corresponding to different burning conditions of the flames have been extracted from a continuous video processing. In this the corresponding temperatures, the Carbon monoxide (CO) emissions and other flue gases have been obtained through measurement. Further the training and testing of Parallel architecture of Radial Basis Function and Back Propagation Algorithm (PRBFBPA) with the data collected have been done and the performance of the algorithms is presented.

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5

Song, Shaoqiu, Jie Lu, Shiqi Xing, Sinong Quan, Junpeng Wang, Yongzhen Li, and Jing Lian. "Near Field 3-D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation." Sensors 22, no. 12 (June 14, 2022): 4509. http://dx.doi.org/10.3390/s22124509.

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In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus.

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6

Vinay, Kumar Jain. "A comparative analysis of neural network function: resilient back propagation algorithm (BPA) and radial basis functions (RBF) in multilingual environment." i-manager's Journal on Digital Signal Processing 10, no. 1 (2022): 9. http://dx.doi.org/10.26634/jdp.10.1.18639.

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The most convenient speech processing tool is Artificial Neural Networks (ANNs). The effectiveness has been tested with various real-time applications. The classifier using artificial neural networks identifies utterances based on features extracted from the speech signal. The proposed approach to multilingual speaker identification consists of two parts, such as a training part and a testing part. In the training part, the classifier is trained using speech feature vectors. The spoken language contains complete information, such as details about the content of the message and details about the speaker of that message. In the present work, the speech signal databases of different speakers in a multilingual environment were recorded in three Indian languages, i.e., Hindi, Marathi, and Rajasthani. The cepstral characteristics of the speech signal were extracted: Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC). The system is designed for speaker recognition through multilingual speech signals using MFCC, GFCC, and combined functions as acoustic characteristics. Training and testing were performed using the Neural Network (NN) function, robust Backpropagation Algorithm (BPA), and Radial Basis Functions (RBF), and the results were compared. The accuracy of the speaker identification system is 94.89% using BPA and 96.62% using the RBF neural network.

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7

EMAM, NAMEER N. EL, and RASHEED ABDUL SHAHEED. "COMPUTING AN ADAPTIVE MESH IN FLUID PROBLEMS USING NEURAL NETWORK AND GENETIC ALGORITHM WITH ADAPTIVE RELAXATION." International Journal on Artificial Intelligence Tools 17, no. 06 (December 2008): 1089–108. http://dx.doi.org/10.1142/s021821300800431x.

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A method based on neural network with Back-Propagation Algorithm (BPA) and Adaptive Smoothing Errors (ASE), and a Genetic Algorithm (GA) employing a new concept named Adaptive Relaxation (GAAR) is presented in this paper to construct learning system that can find an Adaptive Mesh points (AM) in fluid problems. AM based on reallocation scheme is implemented on different types of two steps channels by using a three layer neural network with GA. Results of numerical experiments using Finite Element Method (FEM) are discussed. Such discussion is intended to validate the process and to demonstrate the performance of the proposed learning system on three types of two steps channels. It appears that training is fast enough and accurate due to the optimal values of weights by using a few numbers of patterns. Results confirm that the presented neural network with the proposed GA consistently finds better solutions than the conventional neural network.

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8

Tayfour Ahmed, Amira, Altahir Mohammed, and Moawia Yahia. "Performance comparisons of artificial neural network algorithms in facial expression recognition." International Journal of Engineering & Technology 4, no. 4 (September 13, 2015): 465. http://dx.doi.org/10.14419/ijet.v4i4.5069.

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This paper presents methods for identifying facial expressions. The objective of this paper is to present a combination of texture oriented method with dimensional reduction and use for training the Single-Layer Neural Network (SLN), Back Propagation Algorithm (BPA) and Cerebellar Model Articulation Controller (CMAC) for identifying facial expressions. The proposed methods are called intelligent methods that can accommodate for the variations in the facial expressions and hence prove to be better for untrained facial expressions. Conventional methods have limitations that facial expressions should follow some constraints. To achieve the expression detection accuracy, Gabor wavelet is used in different angles to extract possible textures of the facial expression. The higher dimensions of the extracted texture features are further reduced by using Fisher’s linear discriminant function for increasing the accuracy of the proposed method. Fisher’s linear discriminant function is used for transforming higher-dimensional feature vector into a two-dimensional vector for training proposed algorithms. Different facial emotions considered are angry, disgust, happy, sad, surprise and fear are used. The performance comparisons of the proposed algorithms are presented.

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9

Venkaiah, Chintham, and Mallesham Dulla. "Static security based available transfer capability (ATC) computation for real-time power markets." Serbian Journal of Electrical Engineering 7, no. 2 (2010): 269–89. http://dx.doi.org/10.2298/sjee1002269v.

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In power system deregulation, the Independent System Operator (ISO) has the responsibility to control the power transactions and avoid overloading of the transmission lines beyond their thermal limits. To achieve this, the ISO has to update in real-time periodically Available Transfer Capability (ATC) index for enabling market participants to reserve the transmission service. In this paper Static Security based ATC has been computed for real-time applications using three artificial intelligent methods viz.: i) Back Propagation Algorithm (BPA); ii) Radial Basis Function (RBF) Neural network; and iii) Adaptive Neuro Fuzzy Inference System (ANFIS). These three different intelligent methods are tested on IEEE 24-bus Reliability Test System (RTS) and 75-bus practical System for the base case and critical line outage cases for different transactions. The results are compared with the conventional full AC Load Flow method for different transactions.

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10

Le, Duc Van. "APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS." Science and Technology Development Journal 12, no. 4 (February 28, 2009): 94–106. http://dx.doi.org/10.32508/stdj.v12i4.2237.

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Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

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11

Zhang, Sherong, Ting Liu, and Chao Wang. "Multi-source data fusion method for structural safety assessment of water diversion structures." Journal of Hydroinformatics 23, no. 2 (January 22, 2021): 249–66. http://dx.doi.org/10.2166/hydro.2021.154.

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Abstract Building safety assessment based on single sensor data has the problems of low reliability and high uncertainty. Therefore, this paper proposes a novel multi-source sensor data fusion method based on Improved Dempster–Shafer (D-S) evidence theory and Back Propagation Neural Network (BPNN). Before data fusion, the improved self-support function is adopted to preprocess the original data. The process of data fusion is divided into three steps: Firstly, the feature of the same kind of sensor data is extracted by the adaptive weighted average method as the input source of BPNN. Then, BPNN is trained and its output is used as the basic probability assignment (BPA) of D-S evidence theory. Finally, Bhattacharyya Distance (BD) is introduced to improve D-S evidence theory from two aspects of evidence distance and conflict factors, and multi-source data fusion is realized by D-S synthesis rules. In practical application, a three-level information fusion framework of the data level, the feature level, and the decision level is proposed, and the safety status of buildings is evaluated by using multi-source sensor data. The results show that compared with the fusion result of the traditional D-S evidence theory, the algorithm improves the accuracy of the overall safety state assessment of the building and reduces the MSE from 0.18 to 0.01%.

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12

Chen, Zhanye, Zhiqiang Zeng, Dongning Fu, Yan Huang, Qiang Li, Xin Zhang, and Jun Wan. "Back-Projection Imaging for Synthetic Aperture Radar with Topography Occlusion." Remote Sensing 15, no. 3 (January 26, 2023): 726. http://dx.doi.org/10.3390/rs15030726.

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When synthetic aperture radar (SAR) is conducting remote sensing or terrain mapping, its radar beam is inevitably occluded by the variations in the under-test topography. Although back-projection algorithm (BPA) can theoretically directly solve the imaging problems of topography variations that most current SAR imaging algorithms cannot handle, these BPAs only solve the phase focusing of SAR echo signal, and do not consider the mismatch of SAR imaging results caused by topography occlusion. To solve the mis-imaging issue of the occluded area generated by BPA under the case of topography variation, a topography-based BPA (Topo-BPA) is proposed in this paper. Firstly, a new beam occlusion judgment algorithm based on spherical wave assumption is proposed, and its core is depression angle interpolation and depression angle updating. Then, the proposed Topo-BPA embeds the proposed beam occlusion judgment algorithm before the classical BPA, which not only did not reduce the focus depth of BPA, but improved the imaging accuracy of classical BPA. Finally, numerical experiments have demonstrated the superiority of the Topo-BPA’s performance in comparison with classical BPA.

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13

Chahar, Vikas. "Analysis of Back Propagation Algorithm." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 305–6. http://dx.doi.org/10.15373/22778179/aug2013/98.

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14

A, Dr Deepa, and Fathima Thasliya P A. "Back Propagation." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 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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15

., Bekir Karlik, and Yousif Al-Bastaki . "Materials Matching Using Back-Propagation Algorithm." Information Technology Journal 2, no. 1 (December 15, 2002): 69–71. http://dx.doi.org/10.3923/itj.2003.69.71.

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16

Yun, In-Woo, Hee-ran Lee, and Joon Tae Kim. "An Alternative Approach Obtaining a Normalization Factor in Normalized Min-Sum Algorithm for Low-Density Parity-Check Code." Wireless Communications and Mobile Computing 2018 (October 17, 2018): 1–7. http://dx.doi.org/10.1155/2018/1398191.

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The min-sum algorithm (MSA) for decoding Low-Density Parity-Check (LDPC) code is an approximation algorithm that can greatly reduce the computational complexity of the belief propagation algorithm (BPA). To reduce the error between MSA and BPA, an improved MSA such as normalized min-sum algorithm (NMSA) that uses the normalization factor when updating the check node is used in many LDPC decoders. When obtaining an optimal normalization factor, density evolution (DE) is usually used. However, not only does the DE method require a large number of calculations, it may not be optimal for obtaining a normalization factor due to the theoretical assumptions that need to be satisfied. This paper proposes a new method obtaining a normalization factor for NMSA. We first examine the relationship between the minimum value of variable node messages’ magnitudes and the magnitudes of check node outputs of BPA using the check node message distribution (CMD) chart. And then, we find a normalization factor that minimizes the error between the magnitudes of check node output of NMSA and BPA. We use the least square method (LSM) to minimize the error. Simulation on ATSC 3.0 LDPC codes demonstrates that the normalization factor obtained by this proposed method shows better decoding performance than the normalization factor obtained by DE.

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17

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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18

Kharola, Manisha, and Dinesh Kumar. "Efficient Weather Prediction By Back-Propagation Algorithm." IOSR Journal of Computer Engineering 16, no. 3 (2014): 55–58. http://dx.doi.org/10.9790/0661-16345558.

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19

Hameed, Alaa Ali, Bekir Karlik, and Mohammad Shukri Salman. "Back-propagation algorithm with variable adaptive momentum." Knowledge-Based Systems 114 (December 2016): 79–87. http://dx.doi.org/10.1016/j.knosys.2016.10.001.

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20

Saerens, M., and A. Soquet. "Neural controller based on back-propagation algorithm." IEE Proceedings F Radar and Signal Processing 138, no. 1 (1991): 55. http://dx.doi.org/10.1049/ip-f-2.1991.0009.

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21

Wu, Shiyu, Zhichao Xu, Feng Wang, Dongkai Yang, and Gongjian Guo. "An Improved Back-Projection Algorithm for GNSS-R BSAR Imaging Based on CPU and GPU Platform." Remote Sensing 13, no. 11 (May 27, 2021): 2107. http://dx.doi.org/10.3390/rs13112107.

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Global Navigation Satellite System Reflectometry Bistatic Synthetic Aperture Radar (GNSS-R BSAR) is becoming more and more important in remote sensing because of its low power, low mass, low cost, and real-time global coverage capability. The Back Projection Algorithm (BPA) was usually selected as the GNSS-R BSAR imaging algorithm because it can process echo signals of complex geometric configurations. However, the huge computational cost is a challenge for its application in GNSS-R BSAR. Graphics Processing Units (GPU) provides an efficient computing platform for GNSS-R BSAR processing. In this paper, a solution accelerating the BPA of GNSS-R BSAR using GPU is proposed to improve imaging efficiency, and a matching pre-processing program was proposed to synchronize direct and echo signals to improve imaging quality. To process hundreds of gigabytes of data collected by a long-time synthetic aperture in fixed station mode, a stream processing structure was used to process such a large amount of data to solve the problem of limited GPU memory. In the improvement of the imaging efficiency, the imaging task is divided into pre-processing and BPA, which are performed in the Central Processing Unit (CPU) and GPU, respectively, and a pixel-oriented parallel processing method in back projection is adopted to avoid memory access conflicts caused by excessive data volume. The improved BPA with the long synthetic aperture time is verified through the simulation of and experimenting on the GPS-L5 signal. The results show that the proposed accelerating solution is capable of taking approximately 128.04 s, which is 156 times lower than pure CPU framework for producing a size of 600 m × 600 m image with 1800 s synthetic aperture time; in addition, the same imaging quality with the existing processing solution can be retained.

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22

Zhang, Yu, Jiawen Zhang, Lin Luo, and 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, no. 10 (October 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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23

Ullah, Waheed, and Abid Yahya. "Comprehensive Algorithmic Review and Analysis of LDPC Codes." TELKOMNIKA Indonesian Journal of Electrical Engineering 16, no. 1 (October 1, 2015): 111. http://dx.doi.org/10.11591/tijee.v16i1.1595.

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Due to the increasing popularity of LDPC codes and its demand for future applications, first time in this paper, LDPC coding techniques have been systematically summarized and analyzed. The paper gives the comprehensive review of LDPC encoder, decoder and its architecture for simulation and implementation. The paper is specially intended for giving an insight of the algorithmic overview of the LDPC encoder, decoder and its architecture for research and practical purposes. The original belief propagation algorithm (BPA) , logarithmic model of BPA , and the other simplified form of the logarithmic sum product algorithms (SPA) has been elaborated and analyzed for medium and short length codes under AWGN channel

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24

Miao, Zhi Nong, and Hui Jun Zheng. "An Improved Back-Propagation Algorithm for Fuzzy Modeling." Applied Mechanics and Materials 48-49 (February 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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25

Sharma, Priyanka, and Asha Mishra. "Optimizing Back-Propagation using PSO_Hill_A* and Genetic Algorithm." International Journal of Computer Applications 71, no. 17 (June 26, 2013): 35–41. http://dx.doi.org/10.5120/12453-9181.

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26

Kapoor, V., and Priyanka Gupta. "Digit Recognition System by using Back Propagation Algorithm." International Journal of Computer Applications 83, no. 8 (December 18, 2013): 33–36. http://dx.doi.org/10.5120/14471-2762.

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27

Shah, Tejal B. "Back Propagation Algorithm to Solve Ordinary Differential Equations." International Journal for Research in Applied Science and Engineering Technology 6, no. 5 (May 31, 2018): 1203–5. http://dx.doi.org/10.22214/ijraset.2018.5196.

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28

SUN, Wei-wei. "Adaptive Back-Propagation algorithm with magnified error signals." Journal of Computer Applications 28, no. 8 (August 20, 2008): 2081–83. http://dx.doi.org/10.3724/sp.j.1087.2008.02081.

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29

Fukumi, M., and S. Omatu. "A new back-propagation algorithm with coupled neuron." IEEE Transactions on Neural Networks 2, no. 5 (1991): 535–38. http://dx.doi.org/10.1109/72.134292.

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30

Phansalkar, V. V., and P. S. Sastry. "Analysis of the back-propagation algorithm with momentum." IEEE Transactions on Neural Networks 5, no. 3 (May 1994): 505–6. http://dx.doi.org/10.1109/72.286925.

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31

Savithri, V., and S. Purushothaman. "Intima-media thickness estimation by back propagation algorithm." International Journal of Convergence Computing 1, no. 1 (2013): 3. http://dx.doi.org/10.1504/ijconvc.2013.054656.

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32

Van Ooyen, A., and B. Nienhuis. "Improving the convergence of the back-propagation algorithm." Neural Networks 5, no. 3 (January 1992): 465–71. http://dx.doi.org/10.1016/0893-6080(92)90008-7.

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Chan, L. W., and F. Fallside. "An adaptive training algorithm for back propagation networks." Computer Speech & Language 2, no. 3-4 (September 1987): 205–18. http://dx.doi.org/10.1016/0885-2308(87)90009-x.

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Siu, Sammy, Sheng-Sung Yang, Chien-Min Lee, and Chia-Lu Ho. "Improving the Back-Propagation Algorithm Using Evolutionary Strategy." IEEE Transactions on Circuits and Systems II: Express Briefs 54, no. 2 (February 2007): 171–75. http://dx.doi.org/10.1109/tcsii.2006.883226.

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35

Yi, Jiao-hong, Wei-hong Xu, and Yuan-tao Chen. "Novel Back Propagation Optimization by Cuckoo Search Algorithm." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/878262.

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Анотація:

The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.

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36

Franzi, Edoardo. "Neural accelerator for parallelization of back-propagation algorithm." Microprocessing and Microprogramming 38, no. 1-5 (September 1993): 689–96. http://dx.doi.org/10.1016/0165-6074(93)90212-4.

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37

Negnevitsky, Michael, and Martin J. Ringrose. "Fuzzy Control of Back-Propagation Training." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 6 (November 20, 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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38

Ling, Guobi, Zhiwen Wang, Yaoke Shi, Jieying Wang, Yanrong Lu, and Long Li. "Membrane Fouling Prediction Based on Tent-SSA-BP." Membranes 12, no. 7 (July 4, 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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39

Coyne, Christopher John, Ryan Miller, Kelly Dong, and Rebecca Arielle Shatsky. "Cancer pain in the emergency department: An EMR-based opioid dosing intervention and the effects on patient outcomes." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): e24167-e24167. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.e24167.

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e24167 Background: Pain is a common reason for patients with cancer to seek care in the Emergency Department (ED). Many of these patients are on chronic opioids and may have developed opioid tolerance, which makes appropriate dosing challenging for emergency physicians. Therefore, we designed an electronic medical record (EMR) based best practice advisory (BPA) intervention to recommend appropriate opioid dosing in the ED, based on a patient’s prescription opioid use. Methods: We conducted a retrospective cohort study to evaluate our cancer pain intervention at two academic EDs from May 2020 to May 2022. Our novel BPA algorithm identified ED cancer patients who were taking prescription opioids with an EMR-calculated morphine equivalent daily dose (MEDD) of at least 100. If an ED provider ordered an opioid for one of these patients, the BPA would fire and recommend an opioid dose based on the patient’s individual MEDD. The ED provider would then have the option of accepting or cancelling the BPA. We compared outcomes based on whether patients received BPA-guided increased opioid dosing. We utilized the chi-squared test with an alpha of < 0.05 to assess the relationship between BPA-guided increased opioid dosing and pain scores, hospital admission rates and ED bounce-back rates (return visit within 7 days). Results: We identified 399 patients who met our inclusion criteria, representing 705 BPA alerts. Those patients that received increased opioid dosing were similar to those that did not, with respect to age, sex, race/ethnicity and ECOG score. Patients who received BPA-guided increased opioid dosing experienced a greater improvement in pain (72.5% vs 65.1%, p = 0.03) and were admitted less frequently (58.5% vs 63.9%, p = 0.04). However, among discharged patients, those that received increased opioid dosing bounced back to the ED more frequently (16.2% vs 7.7%, p < .01). Conclusions: This EMR-based BPA intervention was associated with improved pain scores and decreased admission rates among cancer patients visiting the ED. The same intervention, however, was associated with increased ED return visits within 7 days, suggesting that pain management strategies may need to be implemented or modified upon ED discharge to assure that patients’ symptoms are adequately managed at home.

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Junaid, K. A. Mohamed. "Classification Using Two Layer Neural Network Back Propagation Algorithm." Circuits and Systems 07, no. 08 (2016): 1207–12. http://dx.doi.org/10.4236/cs.2016.78104.

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41

Olaru, Adrian, Serban Olaru, and Liviu Ciupitu. "Research of the Neural Network by Back Propagation Algorithm." Advanced Materials Research 463-464 (February 2012): 1151–54. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.1151.

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In the control of the position of the robots systems one of the more important is to assure the minimum errors between the output and the target. All advanced researches in the word propose to use the neural network (NN) and the learning algorithm like Widrow and Hoff, or Levenberg-Marquard by using the least mean square (LMS) of errors and Delta rule, or back propagation training algorithm. Present paper is showing the mathematical model and numerical simulation of some important neurons types used in many applications that require extreme precision and neural network. All assisted researches were made with the owner LabVIEW virtual instrumentation. The research results and virtual LabVIEW instrumentation can be used in many other mechatronics applications.

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42

Goyal, Sheena, and Sheilly Padda. "Back Propagation Algorithm Based Model for Software Cost Estimation." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 6 (June 30, 2017): 509–13. http://dx.doi.org/10.23956/ijarcsse/v7i6/0278.

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43

Nawi, Nazri Mohd, Noorhamreeza Abdul Hamid, Noor Azah Samsudin, Mohd Amin Mohd Yunus, and Mohd Firdaus Ab Aziz. "Second Order Learning Algorithm for Back Propagation Neural Networks." International Journal on Advanced Science, Engineering and Information Technology 7, no. 4 (August 31, 2017): 1162. http://dx.doi.org/10.18517/ijaseit.7.4.1956.

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44

Alshawi, Dr Imad S., Haider Khalaf Allamy, and Dr Rafiqul Zaman Khan. "Development Multiple Neuro-Fuzzy System Using Back-propagation Algorithm." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 6, no. 2 (October 15, 2013): 794–804. http://dx.doi.org/10.24297/ijmit.v6i2.736.

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When fuzzy systems are highly nonlinear or include a large number of input variables, the number of fuzzy rules constituting the underlying model is usually large. Dealing with a large-size fuzzy model may face many practical problems in terms of training time, ease of updating, generalizing ability and interpretability. Multiple Fuzzy System (MFS) is one of effective methods to reduce the number of rules, increase the speed to obtain good results. This paper is therefore proposes another approach call Multiple Neuro-Fuzzy System (MNFS) which can further enhance the performance of the MFS approach. The new approach is used Back-propagation algorithm in the learning process. The performance of the proposed approach evaluates and compares with MFS by three experiments on nonlinear functions. Simulation results demonstrate the effectiveness of the new approach than MFS with regards to enhancement of the accuracy of the results.

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45

Journal, Baghdad Science. "Stability of Back Propagation Training Algorithm for Neural Networks." Baghdad Science Journal 9, no. 4 (December 2, 2012): 713–19. http://dx.doi.org/10.21123/bsj.9.4.713-719.

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In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

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46

Sunaidi, Nik Quosthoni, and Abdulghani Ali Ahmed. "Back Propagation Algorithm-Based Intelligent Model for Botnet Detection." Advanced Science Letters 24, no. 10 (October 1, 2018): 7348–54. http://dx.doi.org/10.1166/asl.2018.12940.

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47

Lee, C. M., S. S. Yang, and C. L. Ho. "Modified back-propagation algorithm applied to decision-feedback equalisation." IEE Proceedings - Vision, Image, and Signal Processing 153, no. 6 (2006): 805. http://dx.doi.org/10.1049/ip-vis:20050139.

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48

Hsi-Chin Hsin, Ching-Chung Li, Mingui Sun, and R. J. Sclabassi. "An adaptive training algorithm for back-propagation neural networks." IEEE Transactions on Systems, Man, and Cybernetics 25, no. 3 (March 1995): 512–14. http://dx.doi.org/10.1109/21.364864.

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Yang, Fei, Pengdong Gao, and Yongquan Lu. "Evolving Resilient Back-Propagation Algorithm for Energy Efficiency Problem." MATEC Web of Conferences 77 (2016): 06016. http://dx.doi.org/10.1051/matecconf/20167706016.

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50

Hassan Mansour, Abdelmajid, Gafar Zen Alabdeen Salh, and Hozayfa Hayder Zeen Alabdeen. "Voice recognition Using back propagation algorithm in neural networks." International Journal of Computer Trends and Technology 23, no. 3 (May 25, 2015): 132–39. http://dx.doi.org/10.14445/22312803/ijctt-v23p128.

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