优化深度网络结构以提高深度网络学习中估计器算法的准确性

IF 1.4 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY
Hamideh Rezaei Nezhad, Farshid Keynia, Amir Sabagh Molahosseini
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引用次数: 0

摘要

基于课堂上的训练和学习过程,形成了基于训练和学习的优化算法。深度神经网络是一种前馈神经网络,其神经元之间的连接模式受动物大脑视觉皮层的启发。本文提出的神经网络方法考虑了减小时间序列类型的预测误差和估计参数的不确定性,改进深度神经网络的结构,提高响应速度;此外,深度神经网络的竞争性能和神经元之间的协作能力也有所提高。所选数据与2016年以后的Qeshm天气(适合研究我们目的的天气条件)预测有关。在本研究中,为了分析不确定剧烈波动模式下的家庭消费电量预测问题,我们决定结合长短期记忆和卷积神经网络两种方法。对于深度网络的训练,采用了BP算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized Deep Networks Structure to Improve the Accuracy of estimator algorithm in Deep Networks learning
An optimization algorithm based on training and learning is formed based on the process of training and learning in a class. A deep neural network is one of the types of feedforward neural networks whose connection pattern among its neurons is inspired by the visual cortex of animals' brain. The present study considers decreasing prediction error for the types of time series and the uncertainty in estimation parameters, improving the structure of the deep neural network and increasing response speed in the proposed neural network method; besides, the competitive performance and the collaboration among the neurons of deep neural network are also increased. Selected data is related to Qeshm weather (suitable weather conditions to study our purpose) prediction during 2016 onwards. In this study, for the purpose of analyzing the prediction issue of power consumption of domestic expenses in the indefinite and severe fluctuation mode, we decided to combine two methods of Long Short-Term Memory and Convolutional Neural Network. For the training of the deep network, the BP algorithm is used.
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来源期刊
Scientia Iranica
Scientia Iranica 工程技术-工程:综合
CiteScore
2.90
自引率
7.10%
发文量
59
审稿时长
2 months
期刊介绍: The objectives of Scientia Iranica are two-fold. The first is to provide a forum for the presentation of original works by scientists and engineers from around the world. The second is to open an effective channel to enhance the level of communication between scientists and engineers and the exchange of state-of-the-art research and ideas. The scope of the journal is broad and multidisciplinary in technical sciences and engineering. It encompasses theoretical and experimental research. Specific areas include but not limited to chemistry, chemical engineering, civil engineering, control and computer engineering, electrical engineering, material, manufacturing and industrial management, mathematics, mechanical engineering, nuclear engineering, petroleum engineering, physics, nanotechnology.
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