عنوان

Associative memory neural networks for error correction of linear block codes

شماره

TLpq231443539

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Associative memory neural networks for error correction of linear block codes

نام عام مواد

[Thesis]

نام نخستين پديدآور

M. A. Sayani

نام ناشر، پخش کننده و غيره

King Fahd University of Petroleum and Minerals (Saudi Arabia)

تاریخ نشرو بخش و غیره

1995

نام خاص و کميت اثر

105

جزئيات پايان نامه و نوع درجه آن

M.S.

کسي که مدرک را اعطا کرده

King Fahd University of Petroleum and Minerals (Saudi Arabia)

امتياز متن

1995

متن يادداشت

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.

موضوع مستند نشده

Applied sciences

موضوع مستند نشده

Artificial intelligence

موضوع مستند نشده

Electrical engineering

مستند نام اشخاص تاييد نشده

M. A. Sayani

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y