Studying possibility in a clustering algorithm for RBFNN design for function approximation

Guillén, Alberto; Pomares, H.; Rojas, Ignacio; González, Jesús; Herrera, José Benavente; Rojas, Fernando; Valenzuela, Olga

doi:10.1007/s00521-007-0134-6

Cited by 22 publications

(12 citation statements)

References 19 publications

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“…In this paper, these parameters are estimated by means of prior knowledge from the clustering method rather than by minimizing the mean square of the training error. It should be mentioned that using the clustering method for initializing the center parameters is not a new idea in RBF-type models, and sophisticated clustering algorithms have been proposed in [30,31]. In the present work, nonlinear parameters are estimated in a clustering way, which have meaningful interpretations.…”

Section: Nonlinear Parameters Estimationmentioning

confidence: 96%

Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Wang

Liang

et al. 2017

Complexity

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Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

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Section: Nonlinear Parameters Estimationmentioning

confidence: 96%

Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Wang

Liang

et al. 2017

Complexity

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“…Second, select the maximal subsethood value as similarity threshold S T i ð Þ: The similarity threshold is defined as (16). …”

Section: Radial Basis Function Neural Network (Rbf-nn)mentioning

confidence: 99%

“…Second, the selected similarity thresholds for each linguistic value are obtained by (16) and are listed in Table 6. For example, the similarity threshold value of assignment in 'Bad' linguistic value can be selected as following.…”

Section: Demonstration Of the Proposed Modelmentioning

confidence: 99%

“…RBF-NN, one type of ANN, applies locally tuned neurons to perform function mappings, and is constructed for several different purposes such as function approximation or curve fitting and pattern recognition [5,[14][15][16][17][18][19][20]. The RBF-NN is a simple and fast learning network, and its nonlinear structures can build model for target dataset without the limitations of statistical methods (statistical distribution assumptions are necessarily made for observations).…”

Section: Introductionmentioning

confidence: 99%

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A new e-learning achievement evaluation model based on RBF-NN and similarity filter

Cheng

Chen

Wei

et al. 2009

Neural Comput & Applic

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As Internet rises fast in recent decades, teaching and learning tools based on Internet technology are rapidly applied in education. Learning through Internet can make learners absorb knowledge without the limitations on learning time and distance. Therefore, in academy, e-learning is one of the popular learning assistant instruments. Recently, ''student-centered'' instruction has become one of the primary approaches in education, and the e-learning system, which can provide the learning environment of personalization and adaptability, is more and more popular. By using e-learning system, teachers can adjust the learning schedule instantly for learners according to their learning achievements, and build more adaptive learning environments. However, in some cases, bias assessments are given for student achievements under specific uncontrollable conditions (i.e. tiredness, preference). In dire need of overcoming this predicament, a new model based on radial basis function neural networks (RBF-NN) and similarity filter to evaluate learning achievements is proposed. The proposed model includes three phases to reduce bias assessments: (1) preprocess: select important features (attributes) to enhance classification performance by feature selection methods and utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data, (2) similarity filter: select linguistic values for each feature and delete inconsistent data by the similarity threshold (similarity filter) and (3) construct classification model and accuracy evaluation: build the proposed model based on RBF-NN and evaluate model performance. To verify the proposed model, a practical achievement dataset, collected from e-learning online examination system in a university of Taiwan, is used as experiment dataset, and the performance of the proposed model is compared with the listing models in this paper. From the empirical study, it is shown that the proposed model provided more proper achievement evaluations than the listing models.

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“…Most of the proposed approaches to determine a priori the RBFNN internal structure are based on clustering methods [1]. A clustering algorithm widely used for this purpose is k-means [2].…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Analysis of Adaptive Clustering Algorithms for the Definition of RBF Neural Network Centers in Regression Problems

Veroneze

Gonçalves

Zuben

2012

Intelligent Data Engineering and Automated Learning - IDEAL 2012

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Abstract.A variety of clustering algorithms have been applied to determine the internal structure of Radial Basis Function Neural Networks (RBFNNs). k-means algorithm is one of the most common choice for this task, although, like many other clustering algorithms, it needs to receive the number of prototypes a priori. This is a nontrivial procedure, mainly for real-world applications. An alternative is to use algorithms that automatically determine the number of prototypes. In this paper, we performed a multiobjective analysis involving three of these algorithms, which are: Adaptive Radius Immune Algorithm (ARIA), Affinity Propagation (AP), and Growing Neural Gas (GNG). For each one, the parameters that most influence the resulting number of prototypes composed the decision space, while the RBFNN RMSE and the number of prototypes formed the objective space. The experiments found that ARIA solutions achieved the best results for the multiobjective metrics adopted in this paper.

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Studying possibility in a clustering algorithm for RBFNN design for function approximation

Cited by 22 publications

References 19 publications

Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

A new e-learning achievement evaluation model based on RBF-NN and similarity filter

A Multiobjective Analysis of Adaptive Clustering Algorithms for the Definition of RBF Neural Network Centers in Regression Problems

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