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首页> 外文期刊>International journal of computer science and network security >Architecture and Weight Optimization of ANN Using Sensitive Analysis and Adaptive Particle Swarm Optimization
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Architecture and Weight Optimization of ANN Using Sensitive Analysis and Adaptive Particle Swarm Optimization

机译：基于敏感分析和自适应粒子群算法的人工神经网络架构和权重优化

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This paper presents an optimized architecture and weights of three layered ANN designing method using sensitivity analysis and adaptive particle swarm optimization (SA-APSO). The optimized ANN architecture determination means to look for near minimal number of neurons in the ANN and finding the efficient connecting weights of it in such a way so that the ANN can achieve better performance for solving different problems. The proposed algorithm designs the ANN into two phases. In the first phase it tries to prune the neurons from ANN using sensitivity analysis to achieve the near minimal ANN structure and therefore it tries to optimize the weight matrices for further performance enhancement by adaptive particle swarm optimization. In the SA phase the authors use impact factor and correlation coefficients for pruning lower salient neurons. Initially it tries to prune the neurons having less impacts in the performance of ANN based on their impact factor values. Therefore it tries to lessen more neurons through merging the similar neurons in the ANN using correlation coefficient among the neuron pairs. In the optimization part it applied adaptive particle swarm optimization to optimize the connecting weight matrices to attain better performance. In the optimization by APSO, a special type of PSO, the authors' use training and validation fitness functions to emphasis on avoiding overfitting and more adapted with ANN, and to achieve effective weight matrices of ANN. To evaluate SA-APSO, it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) to do short term load forecasting (STLF). Results show that the proposed SA-APSO is able to design smaller architecture and attain excellent accuracy.
机译：本文提出了一种使用灵敏度分析和自适应粒子群优化（SA-APSO）的三层ANN设计方法的优化架构和权重。优化的ANN架构确定意味着要在ANN中查找几乎最少数量的神经元，并以这种方式找到其有效的连接权重，以便ANN可以更好地解决不同问题。该算法将人工神经网络设计为两个阶段。在第一阶段，它尝试使用敏感性分析从ANN中修剪神经元，以实现接近最小的ANN结构，因此，它尝试优化权重矩阵，以通过自适应粒子群优化进一步增强性能。在SA阶段，作者使用影响因子和相关系数来修剪下凸神经元。最初，它会尝试根据其影响因子值来修剪对ANN性能影响较小的神经元。因此，它试图通过使用神经元对之间的相关系数合并ANN中的相似神经元来减少更多的神经元。在优化部分中，它应用了自适应粒子群优化来优化连接权重矩阵以获得更好的性能。在由APSO（一种特殊的PSO）进行的优化中，作者使用训练和验证适应度函数来强调避免过度拟合并更适合ANN，并实现ANN的有效权重矩阵。为了评估SA-APSO，将其应用于沙特电力公司西部运营区区域电力控制中心（SEC-WOA）的数据集，以进行短期负荷预测（STLF）。结果表明，所提出的SA-APSO能够设计较小的体系结构并获得出色的精度。

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