Soft Computing Based Signal Prediction, Restoration, and Filtering

Eiji Uchino and Takeshi Yamakawa
Additional contact information
Eiji Uchino: Kyushu Institute of Technology, Dept of Computer Science and Control Engineering
Takeshi Yamakawa: Kyushu Institute of Technology, Dept of Computer Science and Control Engineering

Chapter 14 in Intelligent Hybrid Systems, 1997, pp 331-351 from Springer

Abstract: Abstract In this chapter, soft computational signal processing, especially devoted to prediction, restoration and filtering of signals, is discussed. The neo-fuzzy-neuron, developed by the authors, are applied to the prediction and restoration of damaged signals. The chaotic signals and the speech signals are employed for the experiments. The filtering of noisy signals based on the Radial Basis Function (RBF) network, a special class of a fuzzy neural network, is also discussed. The proposed filter can eliminate not only Gaussian noise but also noise with an arbitrary distribution.

Date: 1997
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-6191-0_14

Ordering information: This item can be ordered from
http://www.springer.com/9781461561910

DOI: 10.1007/978-1-4615-6191-0_14

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().