scholarly journals Comparison of Metamodel Performances on an Electronic Circuit Problem

2021 ◽  
pp. 1-11
Author(s):  
Muzaffer Balaban
Keyword(s):  
Linear Regression ◽  
Radial Basis Function ◽  
Basis Function ◽  
Ordinary Kriging ◽  
Electronic Circuit ◽  
Training Data ◽  
Validation Data ◽  
Data Set ◽  
Simple Kriging ◽  
Radial Basis

Aims: Investigation of building and validation of metamodels which of linear regression, simple kriging, ordinary kriging and radial basis function for an electronic circuit problem are the main aim of this study. Study Design: An electronic circuit problem was considered to compare the performances of the metamodels. Latin hypercube design was used for experimental design of five input variables of the considered problem. Methodology: A training data set consisting of 45 experiments and a validation data set consisting of 500 experiments were obtained using Latin hypercube design. Input variables were used by coded to calculate the spatial distances between observation points more consistently. Then using training data set linear regression, simple kriging, ordinary kriging and radial basis function metamodels were built. And, performance measures were calculated for the validation data set. Results: It has been shown that simple kriging which are applied to outputs the differences from the mean, and ordinary kriging metamodels, produce superior solutions compared to the linear regression and radial basis function metamodels for the electronic circuit problem considered in this study. Prediction superiority of SK and OK than RBF on five-dimensional problem is another important result of the study. Conclusion: Kriging metamodels are considered to be strong alternatives to the other metamodels for the problems that are considered in this study and have a similar nature. Since the superiority of metamodel methods to each other may vary from problem to problem, it is another important issue to compare their performance by considering more than one method in problem solving stage.

scholarly journals Research on Oil Well Production Prediction Based on Radial Basis Function Network

2019 ◽  
Vol 131 ◽  
pp. 01053
Author(s):  
Chuanxin Zhang ◽  
Yunwei Kang ◽  
Jin Chen ◽  
Yunxiang Zhao ◽  
Junxiu Ma
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Network ◽  
Oil Fields ◽  
Training Data ◽  
Oil Well ◽  
Data Set ◽  
Well Production ◽  
Radial Basis ◽  
Function Network

Selection of well and reservoir is an important step in the process of stimulation and transformation of oil fields. Good measures can effectively save the cost in the process of oil field development and greatly increase the production of oil fields. Aiming at the problem of well and reservoir selection in petroleum engineering, a method of oil well production prediction based on radial basis function network is proposed in this paper. According to the field data of Xinjiang oilfield, the main controlling factors with greater influence are selected by correlation analysis after data pretreatment. Then we randomly divide the data into training data set and prediction data set, and use the training data set to create a radial basis function network. Finally, we use the radial basis function network to predict the prediction data set, and the final prediction accuracy reaches 80%.

An efficient hybrid tunicate swarm algorithm and radial basis function searching technique for maximum power point tracking in wind energy conversion system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paulthurai Rajesh ◽  
Francis H. Shajin ◽  
Kumar Cherukupalli
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Training Data ◽  
Data Set ◽  
Content Type ◽  
Wind Energy Conversion ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Radial Basis ◽  
Power Point

Purpose The purpose of this paper is to track the maximal power of wind energy conversion system (WECS) and enhance the search capability for WECS maximum power point tracking (MPPT). Design/methodology/approach The hybrid technique is the combination of tunicate swarm algorithm (TSA) and radial basis function neural network. Findings TSA gets input parameters from the rectifier outputs such as rectifier direct current (DC) voltage, DC current and time. From the input parameters, it enhances the reduced fault power of rectifier and generates training data set based on the MPPT conditions. The training data set is used in radial basis function. During the execution time, it produces the rectifier reference DC side voltage that is converted to control pulses of inverter switches. Originality/value Finally, the proposed method is executed in MATLAB/Simulink site, and the performance is compared with different existing methods like particle swarm optimization algorithm and hill climb searching technique. Then the output illustrates the performance of the proposed method and confirms its capability to solve issues.

scholarly journals PREDIKSI BEBAN ENERGI LISTRIK APJ KOTA SEMARANG MENGGUNAKAN METODE RADIAL BASIS FUNCTION (RBF)

eLEKTRIKA ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 21
Author(s):  
Mukti Dwi Cahyo ◽  
Sri Heranurweni ◽  
Harmini Harmini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Peak Load ◽  
Electricity Demand ◽  
Training Data ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Electric power is one of the main needs of society today, ranging from household consumers to industry. The demand for electricity increases every year. So as to achieve adjustments between power generation and power demand, the electricity provider (PLN) must know the load needs or electricity demand for some time to come. There are many studies on the prediction of electricity loads in electricity, but they are not specific to each consumer sector. One of the predictions of this electrical load can be done using the Radial Basis Function Artificial Neural Network (ANN) method. This method uses training data learning from 2010 - 2017 as a reference data. Calculations with this method are based on empirical experience of electricity provider planning which is relatively difficult to do, especially in terms of corrections that need to be made to changes in load. This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024. The results of this Artificial Neural Network produce projected electricity demand needs in 2019-2024 with an average annual increase of 1.01% and peak load in 2019-2024. The highest peak load in 2024 and the dominating average is the household sector with an increase of 1% per year. The accuracy results of the Radial Basis Function model reached 95%.

scholarly journals Modeling and Optimizing Medium Composition for Shoot Regeneration of Chrysanthemum via Radial Basis Function-Non-dominated Sorting Genetic Algorithm-II (RBF-NSGAII)

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mohsen Hesami ◽  
Roohangiz Naderi ◽  
Masoud Tohidfar
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Shoot Proliferation ◽  
Medium Composition ◽  
Validation Data ◽  
Shoot Number ◽  
Radial Basis

AbstractThe aim of the current study was modeling and optimizing medium compositions for shoot proliferation of chrysanthemum, as a case study, through radial basis function- non-dominated sorting genetic algorithm-II (RBF-NSGAII). RBF as one of the artificial neural networks (ANNs) was used for modeling four outputs including proliferation rate (PR), shoot number (SN), shoot length (SL), and basal callus weight (BCW) based on four variables including 6-benzylaminopurine (BAP), indole-3-butyric acid (IBA), phloroglucinol (PG), and sucrose. Afterward, models were linked to the optimization algorithm. Also, sensitivity analysis was applied for evaluating the importance of each input. The R2 correlation values of 0.88, 0.91, 0.97, and 0.76 between observed and predicted data were obtained for PR, SN, SL, and BCW, respectively. According to RBF-NSGAII, optimal PR (98.85%), SN (13.32), SL (4.83 cm), and BCW (0.08 g) can be obtained from a medium containing 2.16 µM BAP, 0.14 µM IBA, 0.29 mM PG, and 87.63 mM sucrose. The results of sensitivity analysis indicated that PR, SN, and SL were more sensitive to BAP, followed by sucrose, PG, and IBA. Finally, the performance of predicted and optimized medium compositions were tested, and results showed that the difference between the validation data and RBF-NSGAII predicted and optimized data were negligible. Generally, RBF-NSGAII can be considered as an efficient computational strategy for modeling and optimizing in vitro organogenesis.

scholarly journals Implementasi HSV dan GLCM untuk Deteksi Kesegaran Ikan Bawal menggunakan Radial Basis Function Berbasis Android

2019 ◽  
Vol 8 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Muhammad Sarimin ◽  
Nurul Hayaty ◽  
Martaleli Bettiza ◽  
Sapta Nugraha
Keyword(s):  
Radial Basis Function ◽  
Image Recognition ◽  
Basis Function ◽  
Data Center ◽  
Data Centers ◽  
Training Data ◽  
Recognition Method ◽  
Fish Products ◽  
Fresh Fish ◽  
Radial Basis

Tanjungpinang is one of the fish producing cities. fish with a good level of freshness are needed to produce quality fish products. In this case, a system is needed that can recognize fresh and non-fresh fish. In this study using the HSV and GLCM methods as a feature then image recognition is carried out using the Radial Basis Function (RBF). In the RBF recognition method it is necessary to have a central point that becomes the data center. Data center retrieval uses the K-Means method, where this method greatly determines the success of the RBF's introduction. By determining the best number of data centers in the best data center, it is at number 7 with MAD of 0.98. At the time of image acquisition did not pay attention to lighting so as to produce training data with low quality. How in the introduction process using this RBF gets a low level of accuracy, which is equal to 50%

scholarly journals Specialios struktūros daugiasluoksnis perceptronas daugiamačiams duomenims vizualizuoti

2009 ◽  
Vol 50 ◽  
pp. 358-364
Author(s):  
Laura Ringienė ◽  
Gintautas Dzemyda
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Multilayer Perceptron ◽  
Data Clustering ◽  
Multidimensional Data ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Multidimensional Data Visualization ◽  
Radial Basis ◽  
Hidden Layer

Pasiūlytas ir ištirtas radialinių bazinių funkcijų ir daugiasluoksnio perceptrono junginys daugiamačiams duomenis vizualizuoti. Siūlomas vizualizavimo būdas apima daugiamačių duomenų matmenų mažinimą naudojant radialines bazines funkcijas, daugiamačių duomenų suskirstymą į klasterius, klasterį charakterizuojančių skaitinių reikšmių nustatymą ir daugiamačių duomenų vizualizavimą dirbtinio neuroninio tinklo paskutiniame paslėptajame sluoksnyje.Special Multilayer Perceptron for Multidimensional Data VisualizationLaura Ringienė, Gintautas Dzemyda SummaryIn this paper a special feed forward neural network, consisting of the radial basis function layer and a multilayer perceptron is presented. The multilayer perceptron has been proposed and investigated for multidimensional data visualization. The roposedvisualization approach includes data clustering, determining the parameters of the radial basis function and forming the data set to train the multilayer perceptron. The outputs of the last hidden layer are assigned as coordinates of the visualized points.

scholarly journals PERAMALAN JUMLAH TITIK PANAS PROVINSI KALIMANTAN TIMUR MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK

2021 ◽  
Vol 2 (2) ◽  
pp. 64-74
Author(s):  
SITI AISYAH ◽  
SRI WAHYUNINGSIH ◽  
FDT AMIJAYA
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Gaussian Function ◽  
Processing System ◽  
Training Data ◽  
Percentage Error ◽  
Base Function ◽  
East Kalimantan ◽  
Radial Basis

Radial Basis Function Neural Network (RBFNN) is a neural  that uses a radial base function in hidden layers for classification and forecasting purposes. Neural Network is developed into a radial function base with an information processing system that has characteristics similar to biological neural networks, consisting of input layers, hidden layers, and output layers. The data used in this study is data on the number of hotspots in East Kalimantan Province obtained from the official website of the National Aeronautics and Space Administration (NASA). The purpose of this research is to obtain the RBFNN model and the results of forecasting the number of hotspots for the period January 2020 to March 2020. The radial basis function used is the local Gaussian function and the linear activation function. In this study using the proportion of training data and testing data 70: 30; 80:20; and 90:10. The results showed that the input network using significant Partial Autocorrelation Function (PACF) at lag 1 and lag 2, so that the RBFNN model that was formed involved Xt-1 and Xt-2. The best Mean Absolute Percentage Error (MAPE) minimum obtained  the 80:20 data proportion with 2 hidden networks. The RBFNN architecture that is formed is 2 input layers, 2 hidden layers and 1 output layer. Data from forecasting the number of hotspots in East Kalimantan Province shows that from January 2020 to February 2020 there was a decline and March 2020 an increase.

scholarly journals Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function

2018 ◽  
Vol 4 (2) ◽  
pp. 154
Author(s):  
Ian Mochamad Sofian ◽  
Azhar Kholiq Affandi ◽  
Iskhaq Iskandar ◽  
Yosi Apriani
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Training Data ◽  
Rainfall Prediction ◽  
Radial Basis ◽  
Total Data ◽  
Artificial Neural

Two models of Artificial Neural Network (ANN) algorithm have been developed for monthly rainfall prediction, namely the Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN). A total data of 238 months (1994-2013) was used as the input data, in which 190 data were used as training data and 48 data used as testing data. Rainfall data has been tested using architecture BPNN with various learning rates. In addition, the rainfall data has been tested using the RBFNN architecture with maximum number of neurons K = 200, and various error goals. Statistical analysis has been conducted to calculate R, MSE, MBE, and MAE to verify the result. The study showed that RBFNN architecture with error goal of 0.001 gives the best result with a value of MSE = 0.00072 and R = 0.98 for the learning process, and MSE = 0.00092 and R = 0.86 for the testing process. Thus, the RBFNN can be set as the best model for monthly rainfall prediction.

scholarly journals Prediksi Diagnosa Berdasarkan Data Rekam Medis Pasien Menggunakan Support Vector Regression

Repositor ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 393
Author(s):  
Muhammad Nasrul Tsalatsa Putra ◽  
Agus Eko Minarno ◽  
Setio Basuki
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Mean Value ◽  
Training Data ◽  
Support Vector ◽  
Health Examination ◽  
Linear Kernel ◽  
Data Alignment ◽  
Radial Basis

AbstrakRekam medis merupakan suatu berkas dari hasil pemeriksaan kesehatan, pengobatan yang diberikan, tindakan, dan pelayanan lain yang telah diberikan kepada pasien. Penelitian ini dilandasi oleh beberapa permasalahan, diantaranya (1) kurangnya pengawasan, informasi, dan tidak meratanya pemberian layanan kesehatan, (2) terhambatnya perencanaan puskesmas dalam menangulangi kasus yang sudah ada atau yang sering terjadi karena tingginya jumlah dan keberagaman kasus/diagnosa yang ditemukan di masyarakat. Dari permasalahan tersebut dapat diterapkan sistem prediksi diagnosa dengan menerapkan metode Support Vector Regression (SVR). Model SVR yang diterapkan yaitu kernel Linear, kernel Polynomial, serta kernel Radial Basis Function. Pengujian dilakukan dengan membagi dataset ke dalam data uji dan data latih, kumudian dilakukan proses pengujian hingga 9-fold untuk masing-masing model dengan susunan data yang berbeda. Hasil pengujian menunjukkan fungsi kernel RBF memiliki kinerja terbaik dibanding dengan fungsi lainnya dimana nilai NRMSE tertinggi 0.0797 dan nilai akurasi terendah sebesar 0.4826. Hasil prediksi tersebut dapat memberikan sebuah gambaran dan trend mengenai diagnosa yang akan datang berdasarkan data rekam medis pasien. AbstractMedical record is a file of health examination result, medication given, along with treatment and other service given to the patient. This research is based on a few problems, which are: (1) lack of supervising, information, and uneven distribution of health service, (2) delay of health center planning on treating already existing or often occurring case because the number and the variety of case/final diagnose found in society is quite high. From these problems, can be applied a diagnose prediction system is using Support Vector Regression (SVR) method. SVR models used are kernel Linear, kernel Polynomial, and kernel Radial Basis Function. The test is done by dividing dataset into test data and training data, therefore will be conducted process of testing can be done up to 9-fold for each models  with different data alignment. Test result showed kernel RBF function has the best performance among other functions which mean value of NRMSE is 0.0797 and mean value of accuracy is 0.4826. That prediction result can give an illustration and a trend about upcoming diagnose based on patient medical record data.

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