scholarly journals Comparative Study Between Radial Basis Function Neural Network and Random Forest Algorithm for Building Energy Estimation

2019 ◽  
Author(s):  
Che Munira Che Razali ◽  
Amrul Faruq
Keyword(s):  
Random Forest ◽  
Radial Basis Function ◽  
Basis Function ◽  
Comparative Studies ◽  
Computer Experiment ◽  
Absolute Error ◽  
Random Forest Algorithm ◽  
Energy Estimation ◽  
Wall Area ◽  
Radial Basis

Recently, a computer experiment is ubiquitous in modeling and engineering design. Estimation ofenergy building efficiency using computer experiment is widely used to improve performance andenergy consumption in the residential building. This paper proposed Radial Basis Function NeuralNetwork (RBFNN) for energy building consumption dataset and make comparative studies betweenthe Random Forest algorithm (RF) in previous work. This study using the experimental dataset in theliterature that consists of 768 experimental data with eight input variables and two outputparameters of estimation. The inputs variables are relative compactness, surface area, wall area, roofarea, overall height, orientation, glazing area, and glazing area distribution of a building, whileoutput variables include heating and cooling loads of the building. The analytical result of energybuilding performance shows RBFNN is better than RF algorithm in estimation based on errorvalidation calculation using Mean Square Error (MSE), Mean Absolute Error (MAE) and MeanRelative Error (MRE). The findings of this comparative studies found that RBFNN is good in estimationbased on accuracy performance, but the RF algorithm is suitable to determine irrelevant features inestimation by uses many decision trees simultaneously.

scholarly journals Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels

2021 ◽  
Vol 11 (8) ◽  
pp. 3705
Author(s):  
Jie Zeng ◽  
Panayiotis C. Roussis ◽  
Ahmed Salih Mohammed ◽  
Chrysanthos Maraveas ◽  
Seyed Alireza Fatemi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Particle Velocity ◽  
Basis Function ◽  
Absolute Error ◽  
Peak Particle Velocity ◽  
Support Vector ◽  
Effective Parameters ◽  
Experimental Database ◽  
Interaction Detection ◽  
Radial Basis

This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.

scholarly journals Feature Extraction and Classification of Segmented ECG Signals Based on Radial Basis Function and Random Forest Methodology

2021 ◽  
Vol 10 (02) ◽  
pp. 35-45
Author(s):  
Rexy J ◽  
Velmani P ◽  
Rajakumar T.C
Keyword(s):  
Heart Disease ◽  
Random Forest ◽  
Radial Basis Function ◽  
Basis Function ◽  
Performance Metrics ◽  
Heart Diseases ◽  
Ecg Signals ◽  
Specificity And Sensitivity ◽  
Radial Basis ◽  
The Given

Heart disease is the major cause of death ratio increase in this decade. Nowadays various people of different age sector undergo the high risk of heart problems and miss their precious life all of a sudden. Early detection of heart disease will save many people’s life well in advance. Heart Diseases are predictable and they can be identified in earlier stage. First basic method to identify heart disease is ElectroCardioGram (ECG) which is the basic recording method of electrical activities of a functioning heart. ECG is the cheapest and painless method to detect the basic heart problems. This paper is an attempt to detect and classify heart beat signals which will serve as the basic step to predict basic and serious issues which may affect the functioning of the heart. The raw ECG signals are extracted and preprocessed to remove unwanted noises which will produce effective results. The preprocessed ECG signals are then are utilized to identify the heart beats which comprise of signals such as P,Q,R,S,T and U. After detecting the heart beats, they are segmented to extract the ECG Features. The temporal and spectral features are extracted from the segmented ECG signals for classification purpose. The extracted feature vectors are utilized to classify the signals. Radial Basis function and Random Forest method are commonly used classification methodologies; hence these two methodologies are applied to classify the ECG Signals into five basic classes. Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database and Noise Stress database are used for this implementation and the classes are identified based on the given dataset parameters. Performance metrics such as accuracy, specificity and sensitivity are computed to find out the best classification methodology among the applied two methodologies. This performance analysis provides a clear comparative view of both the existing methodologies and specifies that Radial Basis function well suits for the given segmented ECG signals and the extracted features. Hence this performance evaluation paves way for best classification algorithm selection or extension of the best methodology and it can be further optimized for better classification result. The implementation process has been carried out using Matlab software environment.

scholarly journals Solving the Nonlinear Heat Equilibrium Problems Using the Local Multiquadric Radial Basis Function Collocation Method

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1289 ◽  
Author(s):  
Weichung Yeih
Keyword(s):  
Radial Basis Function ◽  
Collocation Method ◽  
Basis Function ◽  
Equilibrium Problems ◽  
Collocation Point ◽  
Absolute Error ◽  
Algebraic Equations ◽  
Radial Basis ◽  
Residual Norm

In this article, the nonlinear heat equilibrium problems are solved by the local multiquadric (MQ) radial basis function (RBF) collocation method. The system of nonlinear algebraic equations is solved by iteration based on the residual norm-based algorithm, in which the direction of evolution is determined by a linear equation. In addition, the role of the collocation point and source point is clearly defined such that in our proposed method the field value of any interested point can be expressed. Six numerical examples are shown to check the performance of the proposed method. As the number of supporting points (mp) increases, the accuracy of numerical solution increases. Among all examples, mp = 50 can perform well. In addition, the selection of shape parameter, c, affects the accuracy. However, as c < 2 the maximum relative absolute error percentage is less than 1%.

Gaussian radial basis functions method for linear and nonlinear convection–diffusion models in physical phenomena

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 69-76
Author(s):  
Fuzhang Wang ◽  
Kehong Zheng ◽  
Imtiaz Ahmad ◽  
Hijaz Ahmad
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Meshless Method ◽  
Absolute Error ◽  
Nonlinear Convection ◽  
Convection Diffusion ◽  
Radial Basis ◽  
Linear And Nonlinear ◽  
Physical Phenomena ◽  
Diffusion Problems

Abstract In this study, we propose a simple direct meshless scheme based on the Gaussian radial basis function for the one-dimensional linear and nonlinear convection–diffusion problems, which frequently occur in physical phenomena. This is fulfilled by constructing a simple ‘anisotropic’ space–time Gaussian radial basis function. According to the proposed scheme, there is no need to remove time-dependent variables during the whole solution process, which leads it to a really meshless method. The suggested meshless method is implemented to the challenging convection–diffusion problems in a direct way with ease. Numerical results show that the proposed meshless method is simple, accurate, stable, easy-to-program and efficient for both linear and nonlinear convection–diffusion equation with different values of Péclet number. To assess the accuracy absolute error, average absolute error and root-mean-square error are used.

scholarly journals Radial basis function neural network for 2 satisfiability programming

2020 ◽  
Vol 18 (1) ◽  
pp. 459 ◽  
Author(s):  
Shehab Alzaeemi ◽  
Mohd. Asyraf Mansor ◽  
Mohd Shareduwan Mohd Kasihmuddin ◽  
Saratha Sathasivam ◽  
Mustafa Mamat
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Squared Error ◽  
Training Technique ◽  
Radial Basis ◽  
Hidden Neurons

<span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.</span>

scholarly journals Prediction Of Use Of Electric Kwh Using Support Vector Regression (SVR) Method (Case Study: Pt Pln (Persero) Rayon Seririt)

Repositor ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 525
Author(s):  
Rima Mediana ◽  
Setio Basuki ◽  
Nur Hayatin
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Radial Basis

AbstrakPeranan listrik sangat penting bagi kehidupan masyarakat, begitu pentingnya peranan listrik tentu saja berdampak pada kebutuhan listrik yang begitu besar, maka PT. PLN (Persero) Rayon Seririt sebagai penyedia tenaga listrik harus bisa memprediksi besarnya peggunaan listrik rumah tangga setiap harinya. Selain itu menyebabkan semakin besar pula pemakian kwh listik, apabila pemakaian kwh listrik tidak diolah dengan baik akan menimbulkan beban energi listrik yang tidak terbendung. Dengan permasalahan yang telah diuraikan, penelitian ini menerapkan algoritma Support Vector Regression dalam Prediksi Pemakain KWH Listrik untuk mengetahui besarnya pemakaian kwh listrik yang akan datang. Berdasarkan hasil pengujian yang dilakukan hasil nilai akurasi terbaik Mean Absolute Error (MAE) sebesar 133560,1, Root Mean Squared Error (RMSE) sebesar 167664,1, dan Koefisien Korelasi sebesar 84,0 pada kernel polynomial. Sehingga algoritma Support Vector Regression dan fungsi kernel Radial Basis Function (RBF) cocok digunakan dalam memprediksi pemakaian kwh listrik.AbstractThe role of electricity is really significant for societies' live and it brings the huge impacts on the needs of electricity. This circumstance makes PT. PLN (Persero) Rayon Seririt as the provider of electricity must be able to predict the amount of household electricity usage steadily. This also causes the greater use of kwh electricity, if the use of kwh electricity is not treated properly, it will cause the burden of electrical energy is unstoppable.  Through the problems that have been elaborated, this study implements the Support Vector Regression algorithm in the prediction of kwh electricity usage to know the amount of  kwh electricity usage that will come.Based on the results of tests that have been conducted,  the result of best accuracy value Mean Absolute Error (MAE) equal to 133560,1, Root Mean Squared Error (RMSE) equal to 133560,1, and Correlation Coefficient equal to 84,0 at Radial Base Function kernel. It means, the Support Vector Regression algorithm and Radial Basis Function kernel function (RBF) are suitable to predict the use of kwh electricity.

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