عنوان

Associative memory neural networks for error correction of linear block codes

Number

TLpq231443539

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Associative memory neural networks for error correction of linear block codes

General Material Designation

[Thesis]

First Statement of Responsibility

M. A. Sayani

Name of Publisher, Distributor, etc.

King Fahd University of Petroleum and Minerals (Saudi Arabia)

Date of Publication, Distribution, etc.

1995

Specific Material Designation and Extent of Item

105

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

King Fahd University of Petroleum and Minerals (Saudi Arabia)

Text preceding or following the note

1995

Text of Note

Associative memory neural networks are used for error correction of linear block codes. The implementation of decoder based on neural networks does not require any special characteristics of codes (i.e., Linearity, cyclic nature etc.) and can decode many different types of codes such as repetition, Hamming, BCH, RS, and other codes. The concept of Hopfield model has been applied for error correction of linear block codes defined over GF(q) fields. All the codewords of length n are considered as stable states which are used to construct the weight matrix as defined in the Hopfield model. All the other possible words of length n are the unstable states. For a linear (n, k) code, the number of stable states are 2 and the possible number of unstable states (patterns) are 2. The decoder would either map the unstable state to one of the stable states or indicates that an error has occurred. The error correction capability is the same as that of classical decoding methods, that is, only limited by the minimum distance constraints. Error correction is applied for the codes having single and multiple error correction capability.

Applied sciences

Artificial intelligence

Electrical engineering

M. A. Sayani

Electronic name

p

[Thesis]

276903

a

Y