Abstract
Radial Basis Function Neural Networks (RBFNN) has been applied successfully to solve function approximation problems. In the design of an RBFNN, it is required a first initialization step for the centers of the RBFs. Clustering algorithms have been used to initialize the centers, but these algorithms were not designed for this task but rather for classification problems. The initialization of the centers is a very important issue that affects significantly the approximation accuracy. Because of this, the CFA (Clustering for Function Approximation) algorithm has been developed to make a better placement of the centers. This algorithm performed very good in comparison with other clustering algorithms used for this task. But it still may be improved taking into account different aspects, such as the way the partition of the input data is done, the complex migration process, the algorithm’s speed, the existence of some parameters that need to be set in a concrete order to obtain good solutions, and the convergence guaranty. In this paper, it is proposed an improved version of this algorithm that solves some problems that affected its predecessor. The approximation of ECG signals is not straightforward since it has low and high frequency components in different intervals of a heart stroke. Furthermore, each interval (P wave, the QRS complex, T wave) is related with the behaviour of specific parts of the heart. The new algorithm has been tested using the ECG signal as the target function to be approximated obtaining very small approximation errors when it is compared with the traditional clustering technique that were used for the centers initialization task. The approximation of the ECG signal can be useful in the diagnosis of certain diseases such as Paroxysmal Atrial Fibrillation (PAF).
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References
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, Nueva York (1981)
Bors, A.G.: Introduction of the Radial Basis Function (RBF) networks. OnLine Symposium for Electronics Engineers
Duda, R.O., Hart, P.E.: Pattern classification and scene analysis. wiley, New York (1973)
Schreier, P.K.G., Marko, W.: An automatic ECG processing algorithm to identify patients prone to paroxysmal atrial fibrillation. Computers in Cardiology 3, 133–136 (2001)
Gersho, A.: Asymptotically Optimal Block Quantization. IEEETransIT 25(4), 373–380 (1979)
González, J., Rojas, I., Pomares, H., Ortega, J., Prieto, A.: A new Clustering Technique for Function Aproximation. IEEETransNN 13(1), 132–142 (2002)
Hartigan, J.A.: Clustering Algorithms. Wiley, New York (1975)
Karayannis, N.B., Mi, G.W.: Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques. IEEE Trans. Neural Networks 8, 1492–1506 (1997)
de Chanzal, C.H.P.: Automated Assessment of Atrial Fibrillation. Computers in Cardiology, 117–120 (2001)
Park, J., Sandberg, J.W.: Universal approximation using radial basis functions network. Neural Computation 3, 246–257 (1991)
Cai, Y., Zhu, Q., Liu, L.: A global learning algorithm for a RBF network.
Rojas, I., Anguita, M., Prieto, A., Valenzuela, O.: Analysis of the operators involved in the definition of the implication functions and in the fuzzy inference proccess. Int. J. Approximate Reasoning 19, 367–389 (1998)
Ros, E., Mota, S., Fernández, F.J., Toro, F.J., Bernier, J.L.: ECG Characterization of Paroxysmal Atrial Fibrillation: Parameter Extraction and Automatic Diagnosis Algorithm. Computers in Biology and Medicine 34(8), 679–696 (2004)
Russo, M.: Improving the LBG Algorithm. In: Mira, J. (ed.) IWANN 1999. LNCS, vol. 1606, pp. 621–630. Springer, Heidelberg (1999)
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Guillén, A., Rojas, I., Ros, E., Herrera, L.J. (2005). Using Fuzzy Clustering Technique for Function Approximation to Approximate ECG Signals. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_55
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DOI: https://doi.org/10.1007/11499305_55
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