基于增量反馈RBM的电能替代终端能源评价模型

发布时间：2018-04-10 20:49

本文选题：电能替代 + 增量反馈　；参考：《计算机工程与设计》2017年11期

【摘要】：针对电能替代终端能源评价方法中存在的效率低、精确度不高等问题,提出一种基于深度学习思想的电能替代终端能源的评价模型。重新设计最优RBM训练算法,提出增量反馈的模型优化迭代策略,优化模型训练参数的初始化赋值,提升电能替代终端能源评价效率和精确度。实验结果表明,对比其它优化算法模型,增量反馈的RBM在抽取和表现特征方面,能够很好将样本的本质特征体现出来,取得了更加高效和准确的评价结果。
[Abstract]:In order to solve the problems of low efficiency and low precision in the energy evaluation method of power substitution terminal, a new evaluation model based on the idea of deep learning is proposed.This paper redesigns the optimal RBM training algorithm, proposes an incremental feedback model optimization iterative strategy, optimizes the initialization assignment of model training parameters, and improves the efficiency and accuracy of energy evaluation for power substitution terminals.The experimental results show that, compared with other optimization algorithms, incremental feedback RBM can well reflect the essential features of samples in terms of extraction and representation features, and obtain more efficient and accurate evaluation results.
【作者单位】：国网北京市电力公司;国网北京市电力公司电力科学研究院;
【分类号】：TK01;TP18

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