Nowadays many applications require text similarity. It becomes important for comparing texts on websites. Keywords are useful for a variety of purposes, including summarizing, indexing, labeling, information retrieval, text similarity, clustering, and searching. The objective of the proposed systemis achieving automatic test for text similarity and compute similarity ratio. The system based on several techniques especially English Morphological Analyzer (EMA). In this work, keyword extraction and text summarization are very useful to determine text similarity for long and very long texts. The proposed system solves the problem of text similarity through applying several statistics and linguistic approaches especially based on morphological rules. The linguistic approaches in this system also include synonym, word-frequencies, word position, and Part-Of-Speech (POS). It will be shown that keyword extraction and text summarization that are built on EMA approach and other statistics and linguistic approaches are very useful in building high accurate method for text similarity. The system was tested and the accuracy rates of results bounded from %58.89 to %111.

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In this paper an adaptive fuzzy system is proposed, it generates the frequency hopping sequence for a spread spectrum communication system. The system learns rules from data and acts as Pseudo Random Number Generator (PRNG) .Thirty sample patterns are the input to Fuzzy PRNG, while the bandwidth is partitioned into number of frequency bins. Each bin used triangular membership function to analyze the input to fuzzy sets, encoding these as fuzzy rules. The input vector matches if-part of a fuzzy rule and fires that rule's output fuzzy set. The fuzzy system tested with 100 and 1025 frequencies and compared it with three other types of PRNG, the fuzzy system had lower values and thus gives a more uniform spread than did the other methods. The fuzzy system was easier to change and harder to intercept.