Skip to main content
SpringerLink
Log in

Comparative Analysis of Radial Basis Functions with SAR Images in Artificial Neural Network

  • Conference paper
Advances in Intelligent Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 320))

Abstract

Radial Basis Functions (RBFs) is used to optimize many mathematical computations. In this paper we have used Gaussian RBF (GRBF), Multi-Quadratic RBF (MQ-RBF), Inverse-Multi-Quadratic RBF (IMQRBF) and q-Gaussian RBF (q-GRBF) to approximate singular values of SAR (Synthetic Aperture Radar) color images. Simulations, mathematical comparisons show that q-Gaussian RBF gives better approximation with respect to the other RBF methods in Artificial Neural Network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Liu, Z., Liu, C.: Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition. Computer Vision and Image Understanding 111(3), 249–262 (2008)

    Article  Google Scholar 

  2. Schölkopf, B., Sung, K.-K., Burges, C.J.C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Signal Process. 45, 2758–2765 (1997)

    Article  Google Scholar 

  3. Mao, K.Z., Huang, G.-B.: Neuron selection for RBF neural network classifier based on data structure preserving criterion. IEEE Trans. Neural Networks 16(6), 1531–1540 (2005)

    Article  Google Scholar 

  4. Luo, F.L., Li, Y.D.: Real-time computation of the eigenvector corresponding to the smallest eigen value of a positive definite matrix. IEEE Trans. on Circuits and Systems 141, 550–553 (1994)

    MathSciNet  Google Scholar 

  5. Klein, C.A., Huang, C.H.: Review of pseudo-inverse control for use with kinematically redundant manipulators. IEEE Trans. Syst. Man Cybern. 13(3), 245–250 (1983)

    Article  Google Scholar 

  6. Paul, A., Bhattacharya, P., Maity, S.P.: Eigen value and it’s comparison with different RBF methods by using MATLAB. In: Sengupta, S., Das, K., Khan, G. (eds.) Emerging Trends in Computing and Communication (ETCC 2014). LNEE, vol. 298, pp. 219–224. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  7. Paul, A., Bhattacharya, P., Maity, S.P.: Comparative study of Radial Basis Function neural network with estimation of Eigenvalue in image using MATLAB. In: Biswas, G.P., Mukhopadhyay, S. (eds.) Recent Advances in Information Technology (RAIT 2014). AISC, vol. 266, pp. 141–146. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  8. Math Works. MATLAB 7.6.0 (R2008a) (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Agartala, India

    Abhisek Paul & Paritosh Bhattacharya

  2. Department of Information Technology, Bengal Engineering and Science University, Shibpur, India

    Santi Prasad Maity

Authors
  1. Abhisek Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paritosh Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Santi Prasad Maity
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abhisek Paul .

Editor information

Editors and Affiliations

  1. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

  2. Indian Institute of Information Technology and Management- - Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  3. Faculty of Design, Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  4. Department of Information Systems and Operations Management, University of Florida, Warrington College of Business, Florida, Florida, USA

    Selwyn Piramuthu

  5. University of Applied Sciences, Institute for Information Systems, Olten, Switzerland

    Thomas Hanne

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Paul, A., Bhattacharya, P., Maity, S.P. (2015). Comparative Analysis of Radial Basis Functions with SAR Images in Artificial Neural Network. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11218-3_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11217-6

  • Online ISBN: 978-3-319-11218-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation