Predicting consumer price index cities and districts in East Java with the gaussian-radial basis function kernel

Rohmah, Mimin Fatchiyatur; Putra, I Ketut Gede Darma; Hartati, Rukmi Sari; Ardiantoro, Luki

doi:10.1088/1742-6596/1456/1/012026

Cited by 4 publications

(3 citation statements)

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“…The BP neural network with 56 nodes is built, and the load prediction model of BP neural network is established to predict the load. Gaussian kernel function was used as the basis function, and penalty function was used to deal with constraints, and RBF neural network was constructed as the prediction model [17]. The input and output nodes of the network are used as input and output signals of the fuzzy system, Neural network algorithm needs to build neural network and needs to go through a lot of iterative process to get the optimal solution.…”

Section: Improve the Traditional Kalman Algorithmmentioning

confidence: 99%

Online Fault Detection of Dry Reactor Based on Improved Kalman Filter

et al. 2022

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In order to meet the requirements of online fault detection for dry reactor, an online fault detection technology based on improved Kalman filter is proposed. The main content of the technology is based on the dry reactor detection technology, through the study of improved Kalman filter, the use of fault diagnosis and other methods, and finally through the experiments and analysis to build improved Kalman filter dry reactor online fault detection research means. The experimental results show that the maximum relative error of the improved Kalman filter is 6.039%, and the average relative error is 2.388%. The improved algorithm is very effective and greatly improves the prediction accuracy. The research based on improved Kalman filter can meet the demand of online fault detection of reactor.

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Section: Improve the Traditional Kalman Algorithmmentioning

confidence: 99%

Online Fault Detection of Dry Reactor Based on Improved Kalman Filter

et al. 2022

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“…MAPE is one of the methods normally used to evaluate a model and its value can be determined using the following equation [34]:…”

Section: -6-mean Absolute Percentage (Mape)mentioning

confidence: 99%

“…where, 𝑦 𝑖 is the i-th data, 𝑦′ 𝑖 is the i-th data for forecasting, and 𝑛 is the total data. The prediction criteria for MAPE as indicated by Rohmah et al [34] are as follows (Table 1):…”

Section: -6-mean Absolute Percentage (Mape)mentioning

confidence: 99%

Implementation of Takagi Sugeno Kang Fuzzy with Rough Set Theory and Mini-Batch Gradient Descent Uniform Regularization

et al. 2023

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The Takagi Sugeno Kang (TSK) fuzzy approach is popular since its output is either a constant or a function. Parameter identification and structure identification are the two key requirements for building the TSK fuzzy system. The input utilized in fuzzy TSK can have an impact on the number of rules produced in such a way that employing more data dimensions typically results in more rules, which causes rule complexity. This issue can be solved by employing a dimension reduction technique that reduces the number of dimensions in the data. After that, the resulting rules are improved with MBGD (Mini-Batch Gradient Descent), which is then altered with uniform regularization (UR). UR can enhance the classifier's fuzzy TSK generalization performance. This study looks at how the rough sets method can be used to reduce data dimensions and use Mini Batch Gradient Descent Uniform Regularization (MBGD-UR) to optimize the rules that come from TSK. 252 respondents' body fat data were utilized as the input, and the mean absolute percentage error (MAPE) was used to analyze the results. Jupyter Notebook software and the Python programming language are used for data processing. The analysis revealed that the MAPE value was 37%, falling into the moderate area. Doi: 10.28991/ESJ-2023-07-03-09 Full Text: PDF

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