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Essentials of Artificial Intelligence


A short overview of artificial intelligence and its relationship with fuzzy logic is provided. We emphasize the role fuzzy logics can play in extending some of the models of Artificial Intelligence.

Training on Artificial Intelligence : Neural Network & Fuzzy Logic Fundamental


Artificial Intelligence (AI) may be regarded as an attempt to understand the processes of perception and reasoning that underlie successful problem solving and to incorporate the result of this research in effective computer programs. At present, AI is largely a collection of sophisticated programming technique that seek to develop systems that attempt to mimic human intelligence without claiming an understanding of the underlying processes involved. Artificial Intelligence (AI) can offer may advantages over traditional methods, such as statistical analysis, particularly where the data exhibits some form of non-linearity. Some existing application of spatial analysis and modeling techniques includes artificial neural networks and rule-based system fuzzy logic . Neural Network are biologically inspired and it is based on a loose analogy of the presumed working of a brain.

Reduction of fuzzy automata by means of fuzzy quasi-orders Artificial Intelligence

In our recent paper we have established close relationships between state reduction of a fuzzy recognizer and resolution of a particular system of fuzzy relation equations. In that paper we have also studied reductions by means of those solutions which are fuzzy equivalences. In this paper we will see that in some cases better reductions can be obtained using the solutions of this system that are fuzzy quasi-orders. Generally, fuzzy quasi-orders and fuzzy equivalences are equally good in the state reduction, but we show that right and left invariant fuzzy quasi-orders give better reductions than right and left invariant fuzzy equivalences. We also show that alternate reductions by means of fuzzy quasi-orders give better results than alternate reductions by means of fuzzy equivalences. Furthermore we study a more general type of fuzzy quasi-orders, weakly right and left invariant ones, and we show that they are closely related to determinization of fuzzy recognizers. We also demonstrate some applications of weakly left invariant fuzzy quasi-orders in conflict analysis of fuzzy discrete event systems.

Fuzzy Logic

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Well, the lecture looks good, however, I have not understood the concepts of these neural networks and fuzzy logic on data analysis area. If we use the same data to model itself, then the question is what we are actually modeling - these simply turn out to be nothing but simply some form of advanced curve fitting tools. The most important feature would be if these tools can give us satisfactory modeling of some variables such as heat transfer coeffecient that cannot be measured easily from some other easily measurable variables, then only it makes sense. If it is the case of estimation and cross validation where the estimation depends on measured quantities, then modeling can be easily achieved such as using some dimensionless physical numbers and why we need the fuzzy logic or neural network in such cases, it is not clear yet.