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Microsoft Visual Studio Case Study

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3.2. Microsoft Visual Studio Microsoft Visual Studio is an integrated development environment (IDE) from Microsoft. It can be used to develop graphical user interface (GUI) applications supported by Microsoft Windows, Windows Mobile, Windows CE, .NET Framework, .NET Compact Framework. Proposed system GUI shown bellows, Figure 16: User interface screen. The GUI is created using Visual C# studio 2005, to display the measured temperature and pulse of the patient. The code written also determines the abnormalities of temperature and heart beat of a patient. C# Implementation code is shown in following section. 3.3. Fuzzy Logic Controller Fuzzy-logic theory has been mainly applied to industrial problems including production systems. There has …show more content…

Configuration Of FLC There is no systematic procedure for the design of an FLC. The configuration consists of four main components: fuzzification interface, knowledge base, decision-making logic, and defuzzification interface. (1) The fuzzification interface transforms input crisp values into fuzzy values and it involves the following functions. • Receives the input values • Transforms the range of values of input variable into corresponding universe of discourse • Converts input data into suitable linguistic values (fuzzy sets). • This component is necessary when input data are fuzzy sets in the fuzzy inference. (2) The knowledge base contains knowledge of the application domain and the control goals. It consists of a data base and a linguistic rule base. • The data base contains necessary definitions which are used in control rules and data manipulation. • The linguistic rule base defines the control strategy and goals by means of linguistic control rules. (3) The decision-making logic performs the following functions simulates the human decision-making procedure based on fuzzy concepts • Infers fuzzy control actions employing fuzzy implication and linguistic …show more content…

Fuzzy Inputs In the fuzzification component, there are three main issues to be considered: scale mapping of input data, strategy for noise and selection of fuzzification functions. (1) Scale mapping of input data: We have to decide a strategy to convert the range of values of input variables into corresponding universe of discourse. When an input value is come through a measuring system,the values must be located in the range of input variables. For example, if the range of input variables was normalized between –1 and +1, a procedure is needed which maps the observed input value into the normalized range. (2) Strategy for noise: When observed data are measured, we may often think that the data were disturbed by random noise. In this case, a fuzzification operator should convert the probabilistic data into fuzzy numbers. In this way, computational efficiency is enhanced since fuzzy numbers are much easier to manipulate than random variables. Otherwise, we assume that the observed data do not contain vagueness,and then we consider the observed data as a fuzzy singleton. A fuzzy singleton is a precise value and hence no fuzziness is introduced by fuzzification in this case. In control applications, the observed data are usually crisp and used as fuzzy singleton inputs in the fuzzy

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