Best Model and Performance of Bayesian Regularization Method for Data Prediction

Setiaji, Pratomo; Widjanarko, Bambang; Syahidin, Yuda; Himawan, Hidayatulah; Siregar, Nurintan Asyiah; Harmayani, Harmayani; Samboteng, Lukman; Gulo, Nur’ Ainun; Kartikaningsih, Reni

doi:10.1088/1742-6596/1933/1/012030

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Bayesian Regularization, also known as backpropagation, is another ANN training algorithm that updates the weights and biases as per the LM optimization [ 40 ]. One-step secant (OSS) algorithm connects the quasi-Newton approach and conjugate gradient algorithms.…”

Section: Ann and Gep Modelsmentioning

confidence: 99%

Machine Learning-Based Fragility Assessment of Reinforced Concrete Buildings

Rasheed¹,

Usman

Zain

et al. 2022

Computational Intelligence and Neuroscience

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In the past, large earthquakes caused the collapse of infrastructure and killed thousands of people in Pakistan, a seismically active region. Therefore, the seismic assessment of infrastructure is a dire need that can be done using the fragility analysis. This study focuses on the fragility analysis of school buildings in Muzaffarabad district, seismic zone-4 of Pakistan. Fragility curves were developed using incremental dynamic analysis (IDA); however, the numerical analysis is computationally time-consuming and expensive. Therefore, soft computing techniques such as Artificial Neural Network (ANN) and Gene Expression Programming (GEP) were employed as alternative methods to establish the fragility curves for the prediction of seismic performance. The optimized ANN model [5-25-1] was used. The feedforward backpropagation network was considered in this study. To achieve a reliable model, 70% of the data was selected for training and 15% for validation and 15% of data was used for testing the model. Similarly, the GEP model was also employed to predict the fragility curves. The results of both ANN and GEP were compared based on the coefficient of determination, R2. The ANN model accurately predicts the global drift values with R2 equal to 0.938 compared to the GEP model having R2 equal to 0.87.

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Section: Ann and Gep Modelsmentioning

confidence: 99%

Machine Learning-Based Fragility Assessment of Reinforced Concrete Buildings

Rasheed¹,

Usman

Zain

et al. 2022

Computational Intelligence and Neuroscience

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“…Penelitian terdahulu yang sudah pernah dilakukan sebelumnya terkait penelitian ini dan menjadi rujukan, antara lain: Penelitian dengan menggunakan algoritma Bayesian regulation untuk menentukan model dan kinerja terbaik dalam menyelesaikan masalah prediksi angka Partisipasi Pendidikan Formal di Indonesia Tahun 2015-2020 yang terdiri dari Angka Partisipasi Sekolah, Angka Partisipasi Kasar, dan Angka Partisipasi Murni. Berdasarkan proses analisis dan perhitungan didapatkan hasil model 2-15-1 yang terbaik dengan epoch 217 iterasi dan MSE sebesar 0,00002945 [10]. Penelitian berikutnya menggunakan algoritma Bayesian regulation untuk mengestimasi Indeks Produksi Industri Mikro dan Kecil menurut KBLI.…”

Section: Pendahuluanunclassified

Penerapan Algoritma Bayesian Regulation untuk Estimasi Posisi Cadangan Devisa Indonesia

Aisyah

Zulkifli

Cakranegara

2022

JoSYC

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Foreign exchange reserves need to be predicted because it is a significant monetary indicator to show the strength or weakness of a country's economic fundamentals. Therefore, the purpose of this study is to estimate the position of Indonesia's foreign exchange reserves at the end of 2022 and 2023 so that the government has benchmarks and information in determining the right economic policy so that the position of foreign exchange reserves remains stable. The estimation algorithm used in this study is the Bayesian Regulation algorithm, one of the Artificial Neural Network algorithms. The research data used is data on the position of Indonesia's foreign exchange reserves (US$ million) obtained from the economic reports of Bank Indonesia. This research will be analyzed using three network architecture models 4-9-1, 4-18-1, and 4-27-1. Based on the analysis of the three models used, the results show that the 4-27-1 model is the best because it has a lower Mean Square Error (MSE) value of 0.00203297. Another benchmark is seen from more minor epochs (iterations) and faster times than the other two models, even though they both produce a 100% accuracy rate. Thus, it can be concluded that the Bayesian Regulation algorithm is good enough to estimate the position of foreign exchange reserves using the 4-27-1 model. Based on the prediction results, the part of Indonesia's foreign exchange reserves at the end of 2022 and 2023 slightly decreased compared to 2021.

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