Abstract: In this paper, we fit a function on probability density curve representing an information stream using artificial neural network . This methodology result is a specific function which represent a memorize able probability density curve . we then use the resulting function for information compression by Huffman algorithm . the difference between the proposed me then with the general methods is , using the Huffman algorithm in several times . In every time , the probability density function is fitted , estimated and then the information representing the function is added to end of the information stream . we next propose two different algorithms for information encoding and decoding using time variable estimation of probability density function . In order to evaluate the proposed algorithm , the percentage of the compression resulting our method has been compared with two popular methods named FDR code [4] and Golomb1 at the end

Keywords: variable time probability density function, neural network, compression information, memorize able information stream, Huffman code

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