Fuzzy Logic: The Principles Of Fuzzy Logic Control System

of the command decisions are based on analytical products and there is a clearly outlined model to insure

Shifting toward computing and software, the company are seeing its products in auspicious fields such as healthcare. The company developed a non-natural smart computer system capable of responding problems posed in an ordinary language called

}\}$ be the corresponding Fuzzy sets defined by the membership function $ \{\mu _{A}^{1},\mu _{A}^{2},\mu _{A}^{3}, . . .\mu _{A}^{m}\}$. The implication of the form $\left ( A,{T_{i}^{A}} \right )\rightarrow \left ( B,{T_{j}^{B}} \right )$ or $A\epsilon {F_{i}^{A}},B\epsilon {F_{j}^{B}}$ is a Fuzzy Association Rule.\\

The Decision Matrix is made up of eight criteria for comparison, to analyze the five concept solutions based on the datum. Accuracy and price were weighted

In this paper, the problem presented will be summarized and include the potential middle-range theory that could be applied. A borrowed theory that can be applied to the problem will be described. Next, a brief history of the borrowed theory’s origin will be provided. Then, examples of the borrowed theory’s previous application will be reviewed. Also, the application of the borrowed theory to the identified problem and how incorporation of the theory can change practice will be discussed. Finally, why application of both the borrowed theory and the middle-range theory can be integrated to create the most appropriate solution to the identified problem will be explained.

And lastly, the decision making element entails making decisions amid multiple options; the idea is looking at all the data and making the most educated decision possible (Baker & Baker, 2014).

Abductive reasoning is mostly considered as a reasoning pattern than a data fusion technique. For any event that has been observed, the abduction method attempts to find the best explanation. Therefore, different inference methods, like NNs or fuzzy logic , can be employed.

Decision making refers to the process of finding and selecting options according to the priorities and values of the person making the decision. Since there are many choices involved, it is important to identify as many options as possible so as to pick the option that best fits a company’s target, goals, values and vision. Due to the integral role of decision making in company growth and financial progress, many firms such as Amazon.com and EBay are pumping in huge investments in business intelligence systems, which are made up of certain technological tools and technological applications that are created for the purpose of facilitating improved decision making process in

Artificial intelligence techniques are increasingly enriching decision support through means as data delivery, analyzing data trends, providing forecasts, developing data consistency, information providing to the exploiter in the most appropriate forms and suggesting courses of action.

There are several methods to solve multi-criteria decision-making problems. Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) are two methods created by Tomas Saaty. AHP endeavor to solve the decision making problem by formed it in a hierarchy while ANP is used when the problem is so complex that cannot be formed as a hierarchy. This complexity happens because of the effect of criteria between each other or the effect of alternatives on criteria. Generally we can say The Analytic Network Process is a generalization of the Analytic Hierarchy Process. The ANP approach can be comprises in to four steps [87]:

1.Introduction In 1965, Zadeh.L.A[16] introduced the study of fuzzy sets. Mathematically a fuzzy set on a set X is a mapping µ into [0, 1] of real numbers; for x in X, µ(x) is called the membership of x belonging to X. The membership function gives only an approximation for belonging but it does not give any information of not belonging. To overcome this, Gau.W.L and Buehrer.D.J[7] introduced the concept of vague sets. A vague set A of a set X is a pair of functions (tA,fA), where tA and fA are fuzzy sets on X satisfying tA(x)+fA(x)≤1, ∀ x ∈ X. A fuzzy set tA of X

ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There are many researches which show that brain store information as pattern. Some of these patterns are very complicated and allows us to recognize from different angles. This paper gives a review of the artificial neural network and analyses the techniques in terms of performance.

Step 3: The basic principles in Step 2 were then extended to calculate the degree of possibility of, Ŝi, of one criterion, being greater than all the other (n- 1) convex fuzzy numbers, Ŝj, of other criteria. This can be defined as follows,

4. To design and implement control strategies like PID, Linear Quadratic Regulator and Model Predictive Control for controlling the tip trajectory

Artificial intelligence and machine learning techniques provide a qualitative as well as quantitative assessment of the power system.