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Routing with Cooperative Nodes Using Improved Learning Approaches

Intelligent Automation & Soft Computing 2023年第3期35卷 2857-2874页

作者： R.Raja N.Satheesh J.Britto Dennis C.Raghavendra Department of CSIT CVR College of EngineeringTelanganaIndia Department of Computer Science and Engineering St.Martin’s Engineering CollegeSecunderabadTelanganaIndia Department of Computer Science and Engineering Dhanalakshmi Srinivasan UniversityTrichyTamilnaduIndia CSIT Department CVR College of EngineeringHyderabadIndia

In IoT,routing among the cooperative nodes plays an incredible role in fulﬁlling the network requirements and enhancing system *** eva-luation of optimal routing and related routing parameters over the deployed net-work environment is *** research concentrates on modelling a memory-based routing model with Stacked long short term memory(s-LSTM)and bi-directional long short term memory(b-LSTM).It is used to hold the routing information and random routing to attain superior *** pro-posed model is trained based on the searching and detection mechanisms to com-pute the packet delivery ratio(PDR),end-to-end(E2E)delay,throughput,*** anticipated s-LSTM and b-LSTM model intends to ensure Quality of Service(QoS)even in changing network *** performance of the proposed b-LSTM and s-LSTM is measured by comparing the signiﬁcance of the model with various prevailing ***,the performance is measured with Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)for mea-suring the error rate of the *** prediction of error rate is made with Learn-ing-based Stochastic Gradient Descent(L-SGD).This gradual gradient descent intends to predict the maximal or minimal error through successive *** simulation is performed in a MATLAB 2020a environment,and the model performance is evaluated with diverse *** anticipated model intends to give superior performance in contrast to prevailing approaches.

关键词： Internet of Things(IoT) stacked long short term memory bi-directional long short term memory error rate stochastic gradient descent

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short-termWind Power Prediction Based on Combinatorial Neural Networks

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Intelligent Automation & Soft Computing 2023年第8期37卷 1437-1452页

作者： Tusongjiang Kari Sun Guoliang Lei Kesong Ma Xiaojing Wu Xian School of Electrical Engineering Xinjiang UniversityUrumqi830017China Anhui Nari Jiyuan Electric Power System Tech Co.LTD Hefei230088China

Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid *** wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid *** the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is ***,the bi-directional long short term memory(biLSTM)network prediction model is constructed,and the bi-directional nature of the biLSTM network is used to deeply mine the wind power data information and find the correlation information within the ***,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and biLSTM network is constructed for the short-term prediction of wind ***,comparing with LSTM,biLSTM,WT-LSTM,WT-biLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and biLSTM network has higher prediction accuracy.

关键词： Wind power prediction wavelet transform back propagation neural network bi-directional long short term memory

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Deep Learning for Wind Speed Forecasting Using bi-LSTM with Selected Features

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Intelligent Automation & Soft Computing 2023年第3期35卷 3829-3844页

作者： Siva Sankari Subbiah Senthil Kumar Paramasivan Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management StudiesChittoor517127India School of Information Technology&Engineering Vellore Institute of TechnologyVellore632014India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computing College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India

Wind speed forecasting is important for wind energy *** the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed *** main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overﬁtting and non-linearity *** curse of dimensionality and overﬁtting issues are handled by using Boruta feature *** uncertainty and the non-linearity issues are addressed by using the deep learning based bi-directional long short term memory(bi-LSTM).In this paper,bi-LSTM with Boruta feature selection named BFS-bi-LSTM is proposed to improve the performance of wind speed *** model identiﬁes relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by bi-LSTM predicts the wind speed by considering the wind speed from the past and future time *** proposed BFS-bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),long short term memory(LSTM),LSTM with Boruta(BFS-LSTM)and bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and *** BFS-bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of *** experimental result shows that the BFS-bi-LSTM produced better forecasting results compared to others.

关键词： bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting

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