Sharif University of Technology Scientia Iranica 1026-3098 31 5 2024 04 01 Optimized deep networks structure to improve the accuracy of estimator algorithm in deep networks learning 417 429 23322 10.24200/sci.2023.62337.7782 EN H. Rezaei Nezhad Department of Computer, Kerman Branch, Islamic Azad University, Kerman, Iran 0000-0002-7726-7171 F. Keynia Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran 0000-0003-3094-6967 A. Sabagh Molahosseini School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK 0000-0002-6024-3202 Journal Article 2023 05 20 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. https://scientiairanica.sharif.edu/article_23322_e940a3ba6df738388b772344a8a112e4.pdf