An active learning reliability method with multiple kernel functions based on radial basis function

2019 ◽  
Vol 60 (1) ◽  
pp. 211-229 ◽  
Author(s):  
Lingjian Shi ◽  
Beibei Sun ◽  
Dauda Sh. Ibrahim
Keyword(s):  
Active Learning ◽  
Radial Basis Function ◽  
Basis Function ◽  
Kernel Functions ◽  
Multiple Kernel ◽  
Reliability Method ◽  
Radial Basis

scholarly journals Comparison between fuzzy kernel k-medoids using radial basis function kernel and polynomial kernel function in hepatitis classification

2021 ◽  
Vol 10 (1) ◽  
pp. 60
Author(s):  
Glori Stephani Saragih ◽  
Sri Hartini ◽  
Zuherman Rustam
Keyword(s):  
Radial Basis Function ◽  
Kernel Function ◽  
Basis Function ◽  
Cross Validation ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Radial Basis ◽  
Rbf Kernel ◽  
Rbf Kernel Function ◽  
Fold Cross Validation

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial kernel function in hepatitis classification. These two kernel functions were chosen due to their popularity in any kernel-based machine learning method for solving the classification task. The hepatitis dataset then used to evaluate the performance of both methods that were expected to provide an accurate diagnosis in patients to obtain treatment at an early phase. The data were obtained from two hospitals in Indonesia, consisting of 89 hepatitis-B and 31 hepatitis-C samples. The data were analyzed using several cases of k-fold cross-validation, and the performances were compared according to their accuracy, sensitivity, precision, F1-Score, and running time. From the experiments, it was concluded that fuzzy kernel k-medoids using RBF kernel function is better compared to polynomial kernel function with the 6% increment of accuracy, 13% enhancement of sensitivity, and 5% improvement in F1-Score. On the other side, the precision of fuzzy kernel k-medoids using polynomial kernel function is 2% higher than using the RBF kernel function. According to the results, the use of RBF or polynomial kernel function in fuzzy kernel medoids can be considered according to the primary goal of the classification.</span></span>

Reliability Based Design Optimization by Using a SLP Approach and Radial Basis Function Networks

2016 ◽  
Author(s):  
Niclas Strömberg
Keyword(s):  
Radial Basis Function ◽  
Design Optimization ◽  
Basis Function ◽  
Physical Space ◽  
Normal Space ◽  
Radial Basis Function Networks ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Reliability Method ◽  
Radial Basis

In this paper reliability based design optimization by using radial basis function networks (RBFN) as surrogate models is presented. The RBFN are treated as regression models. By taking the center points equal to the sampling points an interpolation is obtained. The bias of the network is taken to be known a priori or posteriori. In the latter case, the well-known orthogonality constraint between the weights of the RBFN and the polynomial basis functions of the bias is adopted. The optimization is performed by using a first order reliability method (FORM)-based sequential linear programming (SLP) approach, where the Taylor expansions are generated in intermediate variables defined by the iso-probabilistic transformation. In addition, the reliability constraints are expanded at the most probable points which are found by using Newton’s method. The Newton algorithm is derived by proposing an in-exact Jacobian. In such manner, a FORM-based LP-formulation in the standard normal space of problems with non-Gaussian variables is suggested. The solution from the LP-problem is mapped back to the physical space and the suggested procedure continues in a sequence until convergence is reached. This is implemented for five different distributions: normal, lognormal, Gumbel, gamma and Weibull. It is also presented how the FORM-based SLP approach can be corrected by using second order reliability methods (SORM) and Monte Carlo simulations. In particular, the SORM approach of Hohenbichler is studied. The outlined methodology is both efficient and robust. This is demonstrated by solving established benchmarks as well as finite element problems.

APPLICATION OF GENERALIZED RADIAL BASIS FUNCTION NETWORKS TO RECOGNITION OF RADAR TARGETS

1999 ◽  
Vol 13 (06) ◽  
pp. 945-962 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Classification Performance ◽  
Kernel Functions ◽  
Radial Basis Function Networks ◽  
One Dimensional ◽  
Parzen Window ◽  
Window Functions ◽  
Radial Basis ◽  
Radar Targets

This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive least square-backpropagation (RLS–BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional cross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are introduced. Six generalized kernel functions such as Gaussian, Double-Exponential, Triangle, Hyperbolic, Sinc and Cauchy, are used as the hidden I/O functions of the RBFNCs, and the classification performance of corresponding GRBFNCs for classifying one-dimensional cross profiles of radar targets is discussed.

scholarly journals Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yong Ma ◽  
Hao Liu ◽  
Guangyu Zhai ◽  
Zongjie Huo
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Wireless Network ◽  
Basis Function ◽  
Financial Risk ◽  
Listed Companies ◽  
Kernel Functions ◽  
Support Vector ◽  
Radial Basis

Since the beginning of the new century, risk events such as the world economic crisis have occurred, which have greatly impacted the real economy of our country. A wireless network is a network implemented using wireless communication technology. It includes both global voice and data networks that allow users to establish long-distance wireless connections, as well as infrared technology and radio frequency technology optimized for short-distance wireless connections. These events have a great impact on many small- and medium-sized listed companies, resulting to many small- and medium-sized listed companies going bankrupt. Indeed, one of the important reasons for the frequent bankruptcy of small- and medium-sized listed companies is the lack of awareness of risk prevention and effective financial risk early warning mechanism. The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization. This method shows many unique advantages when dealing with classification problems and has been widely used in many fields. The purpose of this article is to realize the financial risk analysis of listed companies through wireless network communication and the optimal fuzzy SVM artificial intelligence model, which help small- and medium-sized listed companies find abnormalities in their business management activities in advance and deal with market risks in a timely manner. Taking 81 small- and medium-sized listed companies as the research object, this paper chooses the small- and medium-sized listed companies in every quarter of 2018 as the research sample. By using the financial and nonfinancial data of small- and medium-sized listed companies and introducing the support vector machine (SVM) with the fuzzy method, the model of the fuzzy support vector machine (FSVM) is constructed. And the performance of the FSVM under four different kernel functions is compared and studied. At the same time, the performance of the FSVM is compared with other artificial intelligence models. The empirical results show that different kernel functions have different effects on the prediction performance of the FCM-SVM model. Under the Gauss radial basis function, the prediction accuracy of the FCM-SVM is over 86%. It can be seen that in predicting the financial crisis of small- and medium-sized listed companies, the FCM-SVM model with Gauss radial basis function has the best predictive performance. The FSVM model based on Gauss radial basis function not only has the advantages of linearity, being polynomial, and nonlinearity of neurons but also is significantly superior to the traditional artificial intelligence model.

scholarly journals A Novel Artificial Intelligence Technique to Estimate the Gross Calorific Value of Coal Based on Meta-Heuristic and Support Vector Regression Algorithms

2019 ◽  
Vol 9 (22) ◽  
pp. 4868 ◽  
Author(s):  
Hoang-Bac Bui ◽  
Hoang Nguyen ◽  
Yosoon Choi ◽  
Xuan-Nam Bui ◽  
Trung Nguyen-Thoi ◽  
...  
Keyword(s):  
Radial Basis Function ◽  
Support Vector Regression ◽  
Basis Function ◽  
Calorific Value ◽  
Kernel Functions ◽  
Support Vector ◽  
Gross Calorific Value ◽  
Heating Value ◽  
Geological Conditions ◽  
Radial Basis

Gross calorific value (GCV) is one of the essential parameters for evaluating coal quality. Therefore, accurate GCV prediction is one of the primary ways to improve heating value as well as coal production. A novel evolutionary-based predictive system was proposed in this study for predicting GCV with high accuracy, namely the particle swarm optimization (PSO)-support vector regression (SVR) model. It was developed based on the SVR and PSO algorithms. Three different kernel functions were employed to establish the PSO-SVR models, including radial basis function, linear, and polynomial functions. Besides, three benchmark machine learning models including classification and regression trees (CART), multiple linear regression (MLR), and principle component analysis (PCA) were also developed to estimate GCV and then compared with the proposed PSO-SVR model; 2583 coal samples were used to analyze the proximate components and GCV for this study. Then, they were used to develop the mentioned models as well as check their performance in experimental results. Root-mean-squared error (RMSE), correlation coefficient (R2), ranking, and intensity color criteria were used and computed to evaluate the GCV predictive models developed. The results revealed that the proposed PSO-SVR model with radial basis function had better accuracy than the other models. The PSO algorithm was optimized in the SVR model with high efficiency. These should be used as a supporting tool in practical engineering to determine the heating value of coal seams in complex geological conditions.

An Active Learning Approach For Radial Basis Function Neural Networks

2012 ◽  
Author(s):  
S. S. Abdullah ◽  
J. C. Allwright
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Active Learning ◽  
Radial Basis Function ◽  
Model Reduction ◽  
Basis Function ◽  
Cross Validation ◽  
Experiment Design ◽  
New Approach ◽  
Radial Basis

Kertas kerja ini membentangkan satu kaedah Pembelajaran Aktif yang baru untuk melatih Jaringan Saraf Buatan ( JSB) yang berasaskan Fungsi Asas Jejarian (FAJ) apabila JSB tersebut digunakan untuk menyelesaikan masalah Penurunan Model. Kaedah baru ini berasaskan andaian bahawa data yang diperlukan, y, pada input x, berada dalam sebuah set di mana F(x) boleh dibentuk menggunakan pengalaman atau pengetahuan awal tentang satu masalah. Kaedah baru ini akan mendapatkan lokasi data baru dengan meminimumkan ralat kes paling buruk antara keluaran JSB dengan had data seperti yang telah ditakrifkan oleh set F(x). Adalah didapati bahawa kaedah yang dicadangkan ini mampu memberikan kedudukan data baru yang baik pada kes-kes tertentu, berbanding dengan data yang diperolehi daripada kaedah sedia ada. Hasil kajian perbandingan antara kaedah yang dicadangkan dengan kaedah yang sedia ada juga disertakan dalam kertas kerja ini yang menunjukkan bahawa kaedah pembelajaran aktif yang dicadangkan merupakan satu penambahan yang baik kepada kaedah pembelajaran aktif yang sedia ada seperti kaedah reka bentuk maksimum minimum atau kaedah cross validation. Kata kunci: Jaringan saraf buatan, fungsi asas jejarian, penurunan model, kaedah pembelajaran aktif, reka bentuk eksperimen, metamodel This paper presents a new Active Learning algorithm to train Radial Basis Function (RBF) Artificial Neural Networks (ANN) for model reduction problems. The new approach is based on the assumption that the unobserved training data y at input x, lies within a set where F(x) is known from experience or past simulations. The new approach finds the location of the new sample such that the worst case error between the output of the resulting RBF ANN and the bounds of the unknown data as specified by F(x) is minimized. This paper illustrates the new approach for the case when . It was found that it is possible to find a good location for the new data sample by using the suggested approach in certain cases. A comparative study was also done indicating that the new experiment design approach is a good complement to the existing ones such as cross validation design and maximum minimum design. Key words: Artificial neural networks, radial basis functions, model reduction, active learning, experiment design, metamodeling

Sign in / Sign up

Export Citation Format

Share Document