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%.

SMART RISK ANALYST

2019 ◽  
Author(s):  
Dr. Shilpa Laddha-Kabra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Credit Risk ◽  
Radial Basis Function ◽  
Basis Function ◽  
Back Propagation ◽  
Arbitrary System ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This book is an expert system for analyzing credit risk in consumer loan using Artificial Neural Network (ANN). When an individual needs to borrow money, the lender will not only expect repayment but will also want to have confidence that the amount lent can be repaid on time. The effort by the borrower to provide the lender with this confidence level will depend on the amount lent. For lending millions of dollars, the lender may want to take a security interest in assets that have a value in excess of the amount lent to cover fluctuations in the values of those assets during the time the loan is being repaid. When time and foresight permit advance arrangement of loans, the act of borrowing can be made much simpler. When time is short and the need for the loan was not anticipated, the act of going through the process of borrowing may be so time-consuming that obtaining the loan may not be possible at all. Radial Basis Function (RBF), Recurrent Neural Network (RNN), and Back propagation or Multilayer Perceptron (MLP) are the three most popular Artificial Neural Network (ANN) tool for the prediction task. Here the author used both feed forward neural network and radial basis function neural network, back propagation algorithm to make the credit risk prediction. The network can be trained with available data to model an arbitrary system. The trained network is then used to predict the risk in granting the loan. ABOUT THE AUTHOR Dr. Shilpa Laddha-Kabra is Assistant Professor in the Department of Information Technology at Government College of Engineering, Aurangabad (Maharashtra). She is Doctorate (Ph.D.) in Computer Science and Engineering. Her area of interest includes Neural Networks, Information Retrieval, Semantic Web Mining & Ontology and many more. She has a profound expertise in taking the full depth training of engineering students. She has Two Copyrights to her credit & her many research papers are published in prominent international journals.

An Investigation of Artificial Neural Network (ANN) in Quantitative Fault Diagnosis for Turbofan Engine

2000 ◽  
Author(s):  
Sun Bin ◽  
Zhang Jin ◽  
Zhang Shaoji
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Turbofan Engine ◽  
Quantitative Diagnosis ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper is aimed at investigating two kinds of Artificial Neural Network (ANN) applied to quantitative fault diagnosis of turbofan engine gas path components. Among them, one is Back Propagation neural Network (BPN) and the other is Adaptive Probabilistic Neural Network (APNN). Using BPN in order to achieve quantitative fault diagnosis, number of training samples will increase greatly which may lead to the difficulty of iteration convergence. A new learning rule named hybrid rule is introduced to avoid the algorithm falling into static areas and expedite convergence. Recently, a new method to improve the adaptability of multi-layer feed-forward neural network has been developed by the application of Radial Basis Function (RBF). In this paper, the APNN is put forward based on the theory of radial basis function, Bayesian estimation and normal distribution hypothesis of information. It is proposed that the adaptability of APNN can be obtained by applying maximum-likelihood estimation of the output of test case based on a posteriori probability of its input. The investigation shows that BPN and APNN have their own advantages and disadvantages. BPN has faster diagnostic speed and fits the requirement of quantitative diagnosis for single fault. APNN is more adaptive and fit better to quantitative diagnosis for multiple faults.

scholarly journals Penerapan Radial Basis Function Neural Network dalam Pengklasifikasian Daerah Tertinggal di Indonesia

2020 ◽  
Vol 12 (1) ◽  
pp. 37
Author(s):  
Vira Wahyuningrum
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
F Measure

Penetapan daerah tertinggal di Indonesia merupakan kasus pengklasifikasian dengan dua kategori pada variabel respon (biner). Pengklasifikasian dengan metode klasifikasi linier yang umum digunakan yaitu regresi logistik pada tahap eksplorasi data menghasilkan misclassification yang relatif besar, sehingga diperlukan suatu metode alternatif. Artificial Neural Network (ANN) merupakan alternatif yang menjanjikan untuk berbagai metode klasifikasi konvensional. Radial Basis Function Neural Network (RBFNN) merupakan salah satu arsitektur ANN yang populer digunakan dalam klasifikasi. Metode RBFNN menggunakan dua pendekatan yaitu supervised dan unsupervised serta dalam beberapa penelitian menghasilkan akurasi klasifikasi yang tinggi. Penelitian ini bertujuan menerapkan metode RBFNN untuk kasus klasifikasi daerah tertinggal di Indonesia untuk melihat arsitektur RBFNN yang terbentuk dan ketepatan klasifikasi yang dihasilkan. Hasil dari penelitian ini adalah penerapan RBFNN memberikan performa yang sangat baik yaitu nilai akurasi  sebesar 93,48 persen, sensitivitas 81,10 persen dan spesifikasi 97,43 persen. Nilai F-Measure arsitektur RBFNN yang dihasilkan mencapai 85,36 persen.

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

scholarly journals Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Christos Fragopoulos ◽  
Abraham Pouliakis ◽  
Christos Meristoudis ◽  
Emmanouil Mastorakis ◽  
Niki Margari ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Histological Evaluation ◽  
Image Analysis System ◽  
Training Set ◽  
Test Set ◽  
Radial Basis ◽  
Artificial Neural

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

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