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Abstract
In the last decade have been witnessed a great development in artificial intelligence especially in neural network .This paper have been employed neural networks techniques for data compression and decompression. The application of General Regression Neural Network (GRNN) in data compression is very important of data transmission, because this technique offers less than memory storage and time for transferring of the data over computer networks or internet. Taking into consideration the data compression provides security of these data. The matlab version (R2009a ) is used for designing the propose system of neural network (GRNN) to dynamic data compression and decompression .
Highlights
Data compression is an effective means for saving storage space and network bandwidth
In this paper a new idea to text compression based on neural network, the General Regression Neural Network (GRNN) technique used to data compression and decompression
This technique for proposed system offers less than memory storage and time-consuming comparing with the traditional text compression
Summary
Data compression is an effective means for saving storage space and network bandwidth. In order to overcome these difficulties one must resort to universal coding schemes whereby the coding process is interlaced with a learning process for the varying source characteristics Such coding schemes inevitably require a larger working memory space and generally employ performance criteria that are appropriate for a wide variety of sources [7]. The text document is first compressed and the entire document is decompressed when required [1,8] This has some implications such as the unnecessary use of disk space for storing the compressed document as well as uncompressed document at the same time. Another implication is that even though an end user may require only a part of the document, the entire document as a whole is decompressed [1,4]
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