Fuzzy Logic-Based Machines - Case Study Example

14 December 2009 Fuzzy Logic Fuzzy logic and Fuzzy logic based machines are the future of the Artificial Intelligence. This paradigm of the AI offers greater capability to get things done in a more powerful way. By adding the fuzzy logic based capabilities and analysis we can have better control over management and technical capabilities as well as we would be able to perform things that machines and humans are nor able to otherwise do. Fuzzy logic based capabilities are utilized in system analysis, control and design, since it cuts down the time intended for engineering development in the case of enormously complicated systems, is the simply means to resolve the issue. This research will present deep analysis of the fuzzy logic and its functioning areas. Fuzzy logic sorts out suspicions by replicating the process of human’s way of thinking, making the computer to work less accurately and reasonably as compared to traditional computers work. The method of Fuzzy logic is a produced by(Zadeh), and with the passage of time its use is increasing(Nguyen and Walker). The motivation at the back of this technique is that process of decision making is not for all time an issue of black and white, yes or no. However, it often engages a gray area where the technique of fuzzy logic may be is more suitable. Actually, effective decision-making actions are often playful, unstructured, controversial, or confused. In addition, according to professionals, output of decision makers could be developed many times using fuzzy logic. Furthermore, presently, there are only a small number of cases of fully fuzzy logic applications in business, generally in prediction-system behavior. There is an attractive application accessible at yatra.net/solutions; here fuzzy logic is applied to maintain services for corporate travelers. Additionally, fuzzy logic is used in combination with different intelligent systems(Turban, Leidner and McLean 572). Kaehler (2009) states that fuzzy logic is a problem solving process and control system technology that provides itself to accomplishment in systems varying as of small, straightforward, embedded micro-controllers systems to huge, multi-channel computers, networked, or big workstation supported data gathering as well as handling structures. In addition, it can be applied in hardware, software or a mixture of both. Fuzzy logic offers a straightforward method to achieve objective supported by ambiguous, unclear, rough, missing input and noisy information. Fuzzy logics technique to control problems imitates how an individual would formulate decisions, simply and faster (S. D. Kaehler). Fuzzy logic Emergence Hajek (2002) has outlied that term fuzzy logic came out through the development of a theory of fuzzy sets. This theory of fuzzy sets was presented by Lotfi Zadeh(Hajek). Kaehler (2009) has states that Lotfi Zadeh was a professor at the University of California Berkley. He was not able to outline the control methodology; however he stated a method of processing data by permitting partial set association relatively as compared to traditional crisp set membership or else non membership. This technique to set theory was not functional to manage systems until the 1970 because of the inefficient small-computer potentials previous to that time (S. D. Kaehler). Dr. E.H. Mamdani is the main person who has produced the worlds primary fuzzy logic controller that was based on the Dr. Zadehs theory of the fuzzy sets. Dr. Mamdani has stated plainly and confidently that making use of a fuzzy logic regulator intended for speed control of a steam engine was a great deal better to handle the engine through usual mathematically supported control structures as well as logic control hardware. He discovered that making use of the usual technique, wide trial as well as error work was essential to reach at flourishing control intended for an exact speed set-point. Additionally, because of the non-linearity of the steam engine handling capabilities, immediately the speed set-point was transformed, the trial and error attempt had to be made once more to arrive at efficient control. This did not happen by the fuzzy logic controller, that modified a great deal enhanced to transform dissimilarities as well as non-linearity in the system (Fuzzy-Logic). Fuzzy Set and Membership Function Lakshman (2009) states that the membership function of a fuzzy set is a simplification of the pointer fuzzy function in standard sets. In scenario of fuzzy logic, it demonstrates the extent of truth like an addition of assessment. In a fuzzy logic problems degree of truth is sometimes confused by means of likelihoods, while they are theoretically separated, for the reason that fuzzy truth demonstrates membership in unclearly presented sets, not likelihood of a number of condition or event. The idea of membership functions was offered by Zadeh in his initial paper on fuzzy sets in year 1965(Lakshman). Fuzzy Set is some set that permits its members to have dissimilar grades of association (for example membership function) in the interval. Fuzzy set is a great deal of a paradigm transfer the initial attained reception in the far east as well as its flourishing implementation that has guaranteed its implementation all through the world. Fuzzy set is a set of rules and regulations that represents boundaries as well as informs us what to perform to be successful in resolving the problems inside these limits. For instance the utilization of transistors in place of vacuum tubes is a shift of paradigm: in the same way the evolution of Fuzzy set theory as of traditional binary/bivalent set theory is also an example of the paradigm shift (Aziz and Parthiban). Traditional logic rules and fuzzy rules Kaehler (2009) has offered the main difference between the traditional logic rules and fuzzy logic. He states that fuzzy logic encompasses straightforward methods of the rule-based IF A AND B THEN C technique to resolve control difficulty instead of endeavoring to represent a classification mathematically. The fuzzy logic model is foundational method that depends on an operators experience as compared to their technical consideration of the system. For instance, as compared to dealing by temperature control in way of SP =500F T Read More