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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2015

01.10.2015

Online Training for Open Faulty RBF Networks

verfasst von: Yi Xiao, Ruibin Feng, Chi Sing Leung, Pui Fai Sum

Erschienen in: Neural Processing Letters | Ausgabe 2/2015

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Abstract

Recently, a batch mode learning algorithm, namely optimal open weight fault regularization (OOWFR), was developed for handling the open fault situation. In terms of the Kullback–Leibler divergence, this batch mode learning algorithm is optimal. However, the main disadvantage of this batch mode learning algorithm is that it requires to store the entire input–output history. Therefore, the memory consumption is a problem when the number of training samples is large. In this paper, we present an online version for the OOWFR algorithm. We consider two learning rate cases, fixed learning rate and adaptive learning rate. We present the convergent conditions for these two cases. Simulation results show that the performance of the proposed online mode learning algorithm is better than that of other online mode learning algorithms. Also, the performance of the proposed algorithm is close to that of the batch mode OOWFR algorithm.
Vorheriger Artikel A Modified Learning Algorithm for Interval Perceptrons with Interval Weights
Nächster Artikel Multistability and Multiperiodicity for a Class of Cohen–Grossberg BAM Neural Networks with Discontinuous Activation Functions and Time Delays

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Literatur
1.
2.
Chiu CT, Mehrotra K, Mohan CK, Ranka S (1994) Modifying training algorithms for improved fault tolerance. In: Proceedings of The International Conference on Neural Networks 1994, vol. 4. Orlando, pp 333–338
3.
Bernier J, Ortega J, Rodrguez M, Rojas I, Prieto A (1999) An accurate measure for multilayer perceptron tolerance to weight deviations. Neural Process Lett 10(2):121–130CrossRefMATH
4.
Bernier J, Ortega J, Rojas I, Ros E, Prieto A (2000) Obtaining fault tolerant multilayer perceptrons using an explicit regularization. Neural Process Lett 12(2):107–113CrossRef
5.
Zhou ZH, Chen SF (2003) Evolving fault-tolerant neural networks. Neural Comput Appl 11(3–4):156–160CrossRefMATH
6.
Leung CS, Sum J (2008) A fault-tolerant regularizer for RBF networks. IEEE Trans Neural Netw 19(3):493–507CrossRefMATH
7.
Sum JPF, Leung CS, Ho KI-J (2009) On objective function, regularizer, and prediction error of a learning algorithm for dealing with multiplicative weight noise. IEEE Trans Neural Netw 20(1):124–138. doi:10.​1109/​TNN.​2008.​2005596 CrossRefMATH
8.
Leung CS, Wang HJ, Sum J (2010) On the selection of weight decay parameter for faulty networks. IEEE Trans Neural Netw 21(8):1232–1244CrossRefMATH
9.
Chandra P, Singh Y (2003) Fault tolerance of feedforward artificial neural networks—a framework of study. In: Proceedings of The International Joint Conference on Neural Networks 2003, vol 1. Portland, pp 489–494
10.
Phatak DS, Koren I (1995) Complete and partial fault tolerance of feedforward neural nets. IEEE Trans Neural Netw 6(2):446–456CrossRef
11.
Burr J (1995) Digital neural network implementations. In: Neural networks, concepts, applications, and implementations, Vol III. Englewood Cliffs, Prentice Hall, pp 237–285
12.
Himavathi DAS, Muthuramalingam A (2007) Feedforward neural network implementation in FPGA using layer multiplexing for effective resource utilization. IEEE Trans Neural Netw 18:880–888CrossRefMATH
13.
Savich A, Moussa M, Areibi S (2007) The impact of arithmetic representation on implementing MLP-BP on FPGAs: a study. IEEE Trans Neural Netw 18(1):240–252CrossRef
14.
Kaneko T, Liu B (1970) Effect of coefficient rounding in floating-point digital filters. IEEE Trans Aerosp Electron Syst AE–7:995–1003
15.
Liu B, Kaneko T (1969) Error angalysis of digital filter realized with floating-point arithemetic. Proc IEEE 57:1735–1747CrossRef
16.
Bolt G (1991) Fault models for artificial neural networks. IEEE Int Joint Conf Neural Netw 2:1373–1378MATH
17.
Tchernev EB, Mulvaney RG, Phatak DS (2005) Investigating the fault tolerance of neural networks. Neural Comput 17(7):1646–1664MathSciNetCrossRefMATH
18.
Haruhiko T, Masahiko M, Hidehiko K, Terumine H (2007) Enhancing both generalization and fault tolerance of multilayer neural networks. In ATS ’04: Proceedings of IJCNN 2007, pp 1429–1433
19.
Ahmadi A, Sargolzaie M, Fakhraie S, Lucas C, Vakili S (2009) Feedforward sigmoidal networks—equicontinuity and fault-tolerance properties. Comput Eng Technol Int Conf 2:93–97MATH
20.
Sequin C, Clay R (1990) Fault tolerance in artificial neural networks. In: Proceedings of The International Joint Conference on Neural Networks 1990. San Diego, pp 703–708
21.
Neti C, Schneider MH, Young ED (1992) Maximally fault tolerance neural networks. IEEE Trans Neural Netw 3(1):14–23CrossRef
22.
Deodhare D, Vidyasagar M, Sathiya Keerthi S (1998) Synthesis of fault-tolerant feedforward neural networks using minimax optimization. IEEE Trans Neural Netw 9(5):891–900CrossRef
23.
Bernier J, Daz A, Fernndez F, Caas A, Gonzlez J, Martn-Smith P, Ortega J (2003) Assessing the noise immunity and generalization of radial basis function networks. Neural Process Lett 18(1):35–48CrossRef
24.
Cavalieri S, Mirabella O (1999) A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks. Neural Netw 12(1):91–106CrossRef
25.
Mallofre AC, Llanas XP (1996) Fault tolerance parameter model of radial basis function networks. Proc Int Joint Conf Neural Netw 2:1384–1389CrossRef
26.
Parra X, Catala A (2000) Fault tolerance in the learning algorithm of radial basis function networks. Proc IJCNN 3(2000):527–532
27.
Moody JE (1991) Note on generalization, regularization, and architecture selection in nonlinear learning systems. In: Proceedings First IEEE-SP Workshop on Neural Networks for Signal Processing, pp 1–10
28.
Moody J (1996) A smoothing regularizer for feedforward and recurrent neural networks. Neural Comput 8:461–489CrossRef
29.
Krogh A, Hertz JA (1992) A simple weight decay can improve generalization. In Advances in Neural Information Processing Systems. Morgan Kaufmann, Burlington, pp 950–957
30.
Leung CS, Tsoi A, Chan L (2001) Two regularizers for recursive least square algorithms in feedforward multilayered neural networks. IEEE Trans Neural Netw 12(6):1314–1332CrossRef
31.
Kullback S (1959) Information theory and statisticis. Wiley, New York
32.
Wang HJ, Leung CS, Sum PF, Wei G (2010) Kernel width optimization for faulty rbf neural networks with multi-node open fault. Neural Process Lett 32(1):97–107CrossRefMATH
33.
Wang ZQ, Manry M, Schiano J (2000) LMS learning algorithms: misconceptions and new results on convergence. IEEE Trans on Neural Netw 11(1):47–56CrossRef
34.
Luo Z-Q (1991) On the convergence of the LMS algorithm with adaptive learning rate for linear feedforward networks. Neural Comput 3(2):226–245CrossRef
35.
36.
Chen S (2006) Local regularization assisted orthogonal least squares regression. Neurocomputing 69:559–585CrossRef
37.
Sugiyama M, Ogawa H (2002) Optimal design of regularization term and regularization parameter by subspace information criterion. Neural Netw 15(3):349–361CrossRef
38.
Chen S, Cowan CFN, Grant P (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans on Neural Netw 2(2):302–309CrossRefMATH
Metadaten
Titel
Online Training for Open Faulty RBF Networks
verfasst von
Yi Xiao
Ruibin Feng
Chi Sing Leung
Pui Fai Sum
Publikationsdatum
01.10.2015
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2015
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-014-9363-8

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