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3rd World Conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Li, Xiang Gong, Junyi Ni, Xiang Lv, Zheng Bethune First Hospital of Jilin University Changchun China

ISBN: (纸本)9789819791231

Tumor image detection is an important technology in the field of medical imaging, which aims to accurately identify and locate the presence of tumors through the analysis and processing of images and provide an important basis for clinical diagnosis and treatment. In today’s society, there is a problem of low accuracy of tumor image detection, which greatly affects the efficiency and accuracy of tumor image detection. The bp algorithm in neural network is an effective tumor image detection technique. In this paper, an image detection system is created by using the bp algorithm, which greatly improves the accuracy of tumor image detection and improves the efficiency of tumor image detection. Finally, the experimental results show that the bp algorithm is easy to operate and the accuracy rate reaches 98.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： bp algorithm Efficiency Image detection Neural network Tumor

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A serialized civil aircraft R&D cost estimation model considering commonality based on bp algorithm

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Chinese Journal of Aeronautics 2022年第4期35卷 253-265页

作者： Yongjie ZHANG Kang CAO Ke LIANG Yongqi ZENG Wenjun DONG School of Aviation.Northwestern Polytechnical University Xi'an 710072China Shanghai Aircraft Design and Research Institute Shanghai 201210China

The common design of serial civil aircraft, an important strategy of modern civil aircraft research and develop-ment, minimizes the whole life cycle cost of civil aircraft through asset reuse and resource sharing. However, the existing estimating model for the R&D cost of civil aircraft ignores the effects of common design, so the value estimated by estimating derivative models is significantly inconsistent with the actual one. To solve this problem, a novel assessment method for civil aircraft commonality indicators is developed based on fuzzy set in the present study, exploiting the attributes and structural parameters of the aircraft to be assessed as input to determine the degree of membership that pertains to the commonality sub-interval as the commonality *** the bp(Back Propagation) neural network algorithm is adopted to establish the relationship between the common index and the decrease rate of the R&D cost of derivative models. The model employs over a dozen typical civil aircraft models(e.g., Boeing, Airbus, and Bombardier) as the sample data for network learning training to build a mature neural network model for estimating the R&D cost of novel derivative models. As revealed from the comparative analysis on the calculated results of the samples, the estimated results of the model given the effects of commonality in the present study exhibit higher estimation accuracy and value for future work.

关键词： bp algorithm Civil aircraft R&D cost Common indicators Fuzzy set Serialized civil aircraft

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Bistatic Forward-Looking SAR Imaging of Uniformly Moving Target Based on Improved bp algorithm

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2023年 9卷 1006-1017页

作者： Qian, Guangzhao Wang, Yong Zhang, Boya Liu, Mingfan Harbin Inst Technol Res Inst Elect Engn Technol Harbin 150001 Peoples R China

Back projection (bp) algorithm is a common imaging algorithm that processes signals in the time domain. It can be used to create a clear image of a stationary target in the bistatic forward-looking synthetic aperture radar (BFSAR) system. However, when a target has a translational velocity or a complex rotation, the motion of the target causes additional range migration and time delay in the echo data. Hence, the bp algorithm cannot obtain high-resolution images of a maneuvering target. In this paper, a novel algorithm of SAbp is proposed, which is an improved bp algorithm based on the simulated annealing algorithm (SAA) to image a uniformly moving target under the BFSAR model. First, the motion parameters of a moving target are linked with those of the BFSAR model through a mathematical derivation. Then, SAA is applied for estimating the motion parameters of the target, which are used to construct the phase compensated factor for the correction of time delay. Finally, the effectiveness of the proposed algorithm is substantiated by the comparisons of the simulation results between the traditional algorithms and the proposed algorithm.

关键词： Simulated annealing algorithm (SAA) bp algorithm bistatic forward-looking synthetic aperture radar (BFSAR) moving target

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Artificial neural network power system stabiliser trained with an improved bp algorithm

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IEE PROCEEDINGS-GENERATION TRANSMISSION AND DISTRIBUTION 1996年第2期143卷 135-141页

作者： Guan, L Cheng, S Zhou, R NANYANG TECHNOL UNIV SCH ELECT & ELECTR ENGNSINGAPORE 2263SINGAPORE

The paper presents an artificial neural network (ANN) power system stabiliser (NNPSS). The neural network in the proposed NNPSS is trained by an improved bp algorithm. The main difference between the proposed bp algorithm and the conventional bp algorithm is that two variable factors, a learning rate factor epsilon and a momentum factor alpha, are used. This significantly improves the convergence of the ANN's training. A four layer (7-7-4-1) ANN is used to design the NNPSS. The NNPSS is trained by samples obtained from power systems controlled by nonlinear power system stabilisers. The ability of the trained NNPSS to handle unknown disturbances using measurable variables has been investigated in two power systems, a single machine to infinite bus power system and a three machine power system. Test results show that the NNPSS is effective in damping out power system oscillations and is robust to the variations of both the system parameters and the system operating conditions.

关键词： intelligent control bp algorithm power system stabiliser

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The application research of neural network and bp algorithm in stock price pattern classification and prediction

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 115卷 872-879页

作者： Zhang, Dehua Lou, Sha Harbin Univ Commerce Harbin 150028 Heilongjiang Peoples R China

Under the background of big data and Internet finance, quantitative investment is becoming more and more critical, and the prediction of the stock price has become the focus of investors' concern and research. The purpose of this work is to apply neural network and bp algorithm onto the classification and prediction of stock price patterns. The method is to use the bp algorithm neural network for the transaction data of 5 consecutive days as input samples, so there are 20 input layer nodes. The final value of the next day is used as the output sample, and the number of nodes in the output layer is 1. The purpose of network training is to find 20 spline functions. After the training of the bp algorithm neural network, the test data (stock price data for 5 consecutive days) independent of the training data is leveraged as the input of the neural network, and the closing price of the next day is used as the target output of the network. Through the error between the actual output and the target output, the stock price prediction performance of the network model is analyzed. The results have shown that the prediction accuracy of the stock price is 62.12% under the prediction of deep learning fuzzy algorithm and 73.29% under the prediction of the bp algorithm neural network. When the prediction range is between 15 days, the error of 30 prediction values relative to the real value is within +/- 10%, accounting for 90% of the total days, and the prediction effect is the best. By analyzing the prediction of the number of hidden layers on the stock price and different ranges, it can be concluded that the prediction of the stock price trend prediction model of bp algorithm neural network is better than that of the deep learning fuzzy algorithm prediction model. This algorithm provides investors with a certain value for stock forecasting, which makes government gain a more active position in macroeconomic regulation and control. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Neural network bp algorithm Stock price prediction Stock price pattern classification

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An Improved bp algorithm and Its Application in Classification of Surface Defects of Steel Plate

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Journal of Iron and Steel Research International 2007年第2期14卷 52-55页

作者： ZHAO Xiang-yang LAI Kang-sheng DAI Dong-ming Research and Development Division Haler Group Qingdao 266101 Shandong China Department of Physics Dalian University of Technology Dalian 116024 Liaoning China

Artificial neural network is a new approach to pattern recognition and classification. The model of multilayer perceptron （MLP） and back-propagation （bp） is used to train the algorithm in the artificial neural network. An improved fast algorithm of the bp network was presented, which adopts a singular value decomposition （SVD） and a generalized inverse matrix. It not only increases the speed of network learning but also achieves a satisfying precision. The simulation and experiment results show the effect of improvement of bp algorithm on the classification of the surface defects of steel plate.

关键词： artificial neural network MLP bp algorithm SVD generalized inverse matrix

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Non-destructive testing technology for intelligent identification of foreign objects in cosmetics based on bp algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2023年第3期23卷 1395-1407页

作者： Xu, Jing West Anhui Hlth Vocat Coll Dept Med Technol Luan 237000 Anhui Peoples R China

To solve the problem that the presence of foreign matters in cosmetics will affect the safety and health of consumers and is not conducive to the development of the cosmetics industry, an intelligent identification system for foreign matters in cosmetics is established using the improved bp algorithm. Scan cosmetic samples to identify foreign matters and extract foreign matter features, so as to achieve non-destructive detection of foreign matters in cosmetics. Comparing the traditional bp algorithm, Faster R-CNN algorithm and the improved bp algorithm, the results show that the convergence time of the improved bp algorithm is 60 s and 30 s earlier than that of the traditional bp algorithm and Faster R-CNN algorithm respectively;Whether there is noise or not, the recognition rate of the improved bp algorithm is always higher than that of the traditional bp algorithm and Faster R-CNN algorithm. The accuracy rate of the improved bp algorithm is between 0.88 and 0.96, the accuracy rate of the traditional bp algorithm is between 0.57 and 0.75, and the accuracy rate of the Faster R-CNN algorithm is between 0.76 and 0.81. This shows that the improved bp algorithm can realize the nondestructive detection of foreign matters in cosmetics, ensure a high accuracy and fast speed, and provide consumers with a sense of safe use of cosmetics, it can also improve consumers' satisfaction with the use of cosmetic products.

关键词： bp algorithm neural network cosmetic foreign body intelligent recognition non-destructive testing

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A new gene regulatory network model based on bp algorithm for interrogating differentially expressed genes of Sea Urchin

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SPRINGERPLUS 2016年第1期5卷 1-15页

作者： Liu, Longlong Zhao, Tingting Ma, Meng Wang, Yan Ocean Univ China Sch Math Sci Qingdao 266100 Peoples R China Chinese Acad Sci Inst Psychol Key Lab Mental Hlth Beijing 100101 Peoples R China

Background: Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (bp) neural network based on predictive function and network topology. Results: Combined with complex nonlinear mapping and self-learning, the bp neural network was mapped into a complex network. Network characteristics were obtained from the parameters of the average path length, average clustering coefficient, average degree, modularity, and map's density to simulate the real gene network by an artificial network. Through the statistical analysis and comparison of network parameters of Sea Urchin mRNA microarray data under different temperatures, the value of network parameters was observed. Differentially expressed Sea Urchin genes associated with temperature were determined by calculating the difference in the degree of each gene from different networks. Conclusion: The new model we developed is suitable to simulate gene regulatory network and has capability of determining differentially expressed genes.

关键词： bp algorithm Gene regulatory network Neural network model Differentially expressed Sea Urchin genes

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Modeling of mechanical properties of as-cast Mg-Li-Al alloys based on PSO-bp algorithm

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China Foundry 2012年第2期9卷 119-124页

作者： Li Ming Hao Hai Zhang Aimin Song Yingde Liu Zhao Zhang Xingguo School of Materials Science and Engineering Dalian University of TechnologyDalian 116024China

Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research. This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-AI alloys. Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-AI alloys, a momentum back-propagation （bp） neural network with a single hidden layer was established. Particle swarm optimization （PSO） was applied to optimize the bp model. In the neural network, the input variables were the contents of Mg, Li and AI, and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-bp model can describe the quantitative relationship between the Mg-Li-AI alloy＇s composition and its mechanical properties. It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model. The prediction of the influence of AI addition on the mechanical properties of as-cast Mg-Li-AI alloys is consistent with the related research results.

关键词： artificial neural networks Mg-Li-Al alloys bp algorithm particle swarm optimization mechanical properties

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A Modified bp algorithm for LDPC Decoding Based on Minimum Mean Square Error Criterion

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IEICE TRANSACTIONS ON COMMUNICATIONS 2010年第5期E93B卷 1256-1259页

作者： Xu, Meng Ji, Xincun Wu, Jianhui Zhang, Meng Southeast Univ Natl ASIC Res Ctr Nanjing Peoples R China

In this paper, a modified Belief Propagation (bp) decoding algorithm for low-density parity check (LDPC) codes based on minimum mean square error (MMSE) criterion is proposed. This modified algorithm uses linear equation to replace the hyperbolic function in the original bp algorithm and optimizes the linear approximation error based on MMSE criterion. As a result, compared with the standard bp algorithm the computational complexity is reduced significantly as the modified algorithm requires only addition operations to implement. Besides that simulation results show our modified algorithm can achieve an error performance very close to the bp algorithm on the additive white Gaussian noise channel.

关键词： LDPC MMSE bp algorithm linear approximation

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