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A fuzzy logic control system simply is a logic system governed by fuzzy logic (fuzzy in the mathematical sense of the word), in comparison to traditional digital or classical logic systems, which operate on constant values of either 0 or 1. In a classical logic system, for example, an individual number is regarded as true if it falls within a definite range, whereas a fuzzy logic system considers an infinite number of possible values to be true, irrespective of their range. In a fuzzy logic control system, a particular value may be considered to be true if and only if other values that fall within the range are true. Therefore, a classical logic system would consider an infinitely large number of possible values to be true, whereas a fuzzy system would only consider a finite number of values to be true. On the other hand, a fuzzier logic system allows for extremely vast ranges of permissible values, thereby eliminating the need for a bound variable.

The implementation of fuzzy logic is done using the Control Circuitry Language (CCL) framework. For instance, in fuzzy URL, a URL query results in a list of fuzzy URLs if the given keyword expression is satisfied. A corresponding fuzzy URL handler processes the request and extracts relevant information from the URL. The information extracted by the handler is then fed into a data storage device. The data storage device is used to store and retrieve information from the URL’s indexed web pages. The resulting information is used by a fuzzy software application that uses the knowledge of the web pages to make predictions about the relevance of upcoming messages in the inbox.

The two major areas where fuzzy sets are used in practice are in the fields of Natural Language Processing (NLP), and Information Retrieval (ERP). In the NLP area, fuzzy sets are used to leverage on the existing rich voice of the human voice experience and apply it to generating rich language responses, such as voice recognition and audio transcription. In ERP, fuzzy sets are used in conjunction with large scale memory optimization (HMO), to leverage on the existing large scale in the data management domain.

The underlying principle behind fuzzy logic is that vagueness adds to the effectiveness of analog reasoning. The more vagueness there is in an idea, the more permeable it becomes to other, less knowledgeable minds, and the more effective it is in communications. Fuzzy sets represent the abstract nature of mathematical truth, and they also represent mathematical concepts that can be understood intuitively.

Fuzzy Logic has a number of open source applications that have been developed internally at Microsoft to implement the logic using simple programs that a non-technical person can easily follow. One such popular application area is the Bing Search Engines’ geo-targeting capabilities. Bing includes fuzzy data for millions of locations across the world. With these technologies, analysts and search marketers can specify key terms in an Internet Explorer search field, and the search engine will generate localized results based on fuzzy logic.

Another example is using fuzzy logic function with traditional citation needed analysis. A business may be interested in knowing if there is a correlation between geographic location and sales revenue. In this case, all a business has to do is to create a data set that contains the information needed by the company, and then associate that information to a business decision.