| 改进量子粒子群BP神经网络参数优化及应用 |
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| 引用本文： | 贾伟,赵雪芬.改进量子粒子群BP神经网络参数优化及应用[J].教育技术导刊,2019,18(10):30-35. |
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| 作者姓名： | 贾伟 赵雪芬 |
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| 作者单位： | 宁夏大学新华学院 信息与计算机科学系，宁夏 银川 750021 |
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| 基金项目： | 宁夏高等学校科学技术研究项目（NGY2017225） |
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| 摘 要： | 针对现有BP神经网络选取权值和阈值不精确问题，采用改进量子粒子群优化算法优化BP神经网络权值和阈值。首先在改进的量子粒子群优化算法中，采用双层多种群优化策略提高整个种群的寻优能力，然后在每个子群中使用混沌反向学习和Levy飞行增强子群寻优能力，最后利用改进的量子粒子群优化BP神经网络权值和阈值。实验结果表明，改进的量子粒子群优化算法能有效提高BP神经网络的全局寻优能力和收敛性，对数控高速铣削加工的铣削力进行准确预测。
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| 关 键 词： | 量子粒子群优化 BP神经网络 多种群 混沌反向学习 Levy飞行 |
| 收稿时间： | 2019-01-02 |
Parameters Optimization of BP Neural Network Using Improved Quantum-behaved Particle Swarm and Its Application |
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| JIA Wei,ZHAO Xue-fen.Parameters Optimization of BP Neural Network Using Improved Quantum-behaved Particle Swarm and Its Application[J].Introduction of Educational Technology,2019,18(10):30-35. |
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| Authors: | JIA Wei ZHAO Xue-fen |
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| Institution: | Faculty of Information and Computer Science， Xinhua College of Ningxia University， Yinchuan 750021， China |
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| Abstract: | Focused on the issue that the existing BP neural networks cannot effectively select weights and thresholds， an improved quantum-behaved particle swarm optimization is proposed to optimize the weights and thresholds of BP neural network. Firstly， in the improved quantum-behaved particle swarm optimization， a two-layer multi-swarm strategy is used to improve the global search ability. Secondly， chaotic opposition-based learning and levy flight are utilized in each sub-swarm to enhance the searching ability of sub-swarm. Finally， the improved quantum-behaved particle swarm optimization is used to optimize the weights and thresholds of BP neural network. Experimental results show that the proposed quantum-behaved particle swarm optimization can effectively improve the global optimization ability and convergence of BP neural network， and can accurately predict the milling force of NC high-speed milling. |
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| Keywords: | quantum-behaved particle swarm optimization BP neural network multi-swarm chaotic opposition-based learning Levy flight |
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