scholarly journals COMPARISON OF MULTI LAYER PERCEPTRON (MLP) AND RADIAL BASIS FUNCTION (RBF) FOR CONSTRUCTION COST ESTIMATION: THE CASE OF TURKEY

2015 ◽  
Vol 22 (4) ◽  
pp. 480-490 ◽  
Author(s):  
Savas BAYRAM ◽  
M. Emin OCAL ◽  
Emel LAPTALI ORAL ◽  
C. Duran ATIS
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Cost Estimation ◽  
Construction Projects ◽  
Construction Cost ◽  
Multi Layer Perceptron ◽  
Radial Basis ◽  
Artificial Neural Network Ann ◽  
Public Construction ◽  
The Cost

In Turkey, for the preliminary construction cost estimation, a notice, which is updated and published annu­ally by Turkish Ministry of the Environment and Urbanism, known as “unit area cost method” (UACM) is generally employed. However, it’s known that the costs obtained through this method in which only construction area is taken into consideration have significant differences from actual costs. The aim of this study is to compare the cost estimations obtained through “multi layer perceptron” (MLP) and “radial basis function” (RBF), which are commonly used artificial neural network (ANN) methods. The results of MLP and RBF were also compared with the results of UACM and the validity of UACM was interpreted. Dataobtained from 232 public construction projects, which completed between 2003 and 2011 in different regions of Turkey, were reviewed. Consequently, estimated costs obtained from RBF were found to be higher than the actual costs with a 0.28% variance, while the estimated costs obtained from MLP were higher than actual values with a 1.11% variance. The approximate costs obtained from UACM are higher than actual costs with a 28.73% variance. It was found that both ANN methods were showed better performance than the UACM but RBF was superior to MLP.

scholarly journals Using Evolving ANN-Based Algorithm Models for Accurate Meteorological Forecasting Applications in Vietnam

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Tim Chen ◽  
N. Kapron ◽  
J. C.-Y. Chen
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Time Variation ◽  
Tidal Waves ◽  
Radial Basis ◽  
Physically Based ◽  
Artificial Neural Network Ann ◽  
Meteorological Tsunamis

The reproduction of meteorological tsunamis utilizing physically based hydrodynamic models is complicated in light of the fact that it requires large amounts of information, for example, for modelling the limits of hydrological and water driven time arrangement, stream geometry, and balanced coefficients. Accordingly, an artificial neural network (ANN) strategy utilizing a backpropagation neural network (BPNN) and a radial basis function neural network (RBFNN) is perceived as a viable option for modelling and forecasting the maximum peak and variation with time of meteorological tsunamis in the Mekong estuary in Vietnam. The parameters, including both the nearby climatic weights and the wind field factors, for finding the most extreme meteorological waves, are first examined, through the preparation of evolved neural systems. The time series of meteorological tsunamis were used for training and testing the models, and data for three cyclones were used for model prediction. Given the 22 selected meteorological tidal waves, the exact constants for the Mekong estuary, acquired through relapse investigation, are A = 9.5 × 10−3 and B = 31 × 10−3. Results showed that both the Multilayer Perceptron Network (MLP) and evolved radial basis function (ERBF) methods are capable of predicting the time variation of meteorological tsunamis, and the best topologies of the MLP and ERBF are I3H8O1 and I3H10O1, respectively. The proposed advanced ANN time series model is anything but difficult to use, utilizing display and prediction tools for simulating the time variation of meteorological tsunamis.

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.

Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

2012 ◽  
Vol 109 (3-4) ◽  
pp. 519-528 ◽  
Author(s):  
Mehdi Rezaeian-Zadeh ◽  
Shahrookh Zand-Parsa ◽  
Hirad Abghari ◽  
Masih Zolghadr ◽  
Vijay P. Singh
Keyword(s):  
Radial Basis Function ◽  
Air Temperature ◽  
Basis Function ◽  
Arid Regions ◽  
Radial Basis Function Networks ◽  
Multi Layer Perceptron ◽  
Radial Basis ◽  
Semi Arid
Sign in / Sign up

Export Citation Format

Share Document