Optimizing RBF Networks with Cooperative/Competitive Evolution of Units and Fuzzy Rules

Rivas, Antonio Jesús Rivera; Ortega, Julio; Rojas, Ignacio; Prieto, Alberto

doi:10.1007/3-540-45720-8_68

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…The width estimation method in (14) is used as the baseline with further integration of spatial distribution and nonlinearity features of the target function. The power exponent "114" is devised to approximate the order of magnitude of the second derivative of (4) with respect to x.…”

Section: )mentioning

confidence: 99%

Euclidean distance and second derivative based widths optimization of radial basis function neural networks

Yao

Chen

Tooren

et al. 2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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The design of radial basis function widths of Radial Basis Function Neural Network (RBFNN) is thoroughly studied in this paper. Firstly, the influence of the widths on performance ofRBFNN is illustrated with three simple function approximation experiments. Based on the conclusions drawn from the experiments, we find that two key factors including the spatial distribution of the training data set and the nonlinearity of the function should be considered in the width design. We propose to use Euclidean distances between center nodes and the second derivative of function to measure these two factors respectively. Secondly, a two step method is proposed to design the widths based on the information about the aforementioned two key factors obtained from comprehensive analysis of the given training data set. In the first step the data set spatial distribution features are analyzed according to the Euclidean distances between the data points, and the second derivative of each center node is estimated with finite difference approximation method. Based on the analysis an initial design of the widths is given with a heuristic equation. In the second step optimization techniques are used to optimize the widths which can effectively find the optimum with the good initial baseline.Thirdly, one mathematical example is taken to verify the efficiency of the proposed method, and followed by conclusions.

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Section: )mentioning

confidence: 99%

Euclidean distance and second derivative based widths optimization of radial basis function neural networks

Yao

Chen

Tooren

et al. 2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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“…Architecture parameters (apart from features, see below) are optimized in about half of the publications. The number of centers (hidden neurons) is considered in [17]- [20], [25], [29]- [31], [33], [40], [42], [45], [46], [50], [52], [54], [57], [62], [67], [68], [70], [71], [73], [75], and [77], for instance. Other architecture parameters are the type of basis functions [57], [58], the training time (epoch number) [36], and parameters of training algorithms [26], [27], [36], [63].…”

Section: A Related Workmentioning

confidence: 99%

“…Similar methods are introduced in [28], [67], and [69]. Also, [77] describes an approach that takes cooperative-competitive evolution into account. The fitness function combines concepts such as cooperation, speciation, and niching.…”

Section: A Related Workmentioning

confidence: 99%

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Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications

Buchtala

Klimek²,

Sick

2005

IEEE Trans. Syst., Man, Cybern. B

108

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In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

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Technical data mining with evolutionary radial basis function classifiers

Bauer¹,

Buchtala²,

Horeis³

et al. 2009

Applied Soft Computing

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Optimizing RBF Networks with Cooperative/Competitive Evolution of Units and Fuzzy Rules

Cited by 4 publications

References 13 publications

Euclidean distance and second derivative based widths optimization of radial basis function neural networks

Euclidean distance and second derivative based widths optimization of radial basis function neural networks

Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications

Technical data mining with evolutionary radial basis function classifiers

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