Fuzzy logic

Fuzzy logic is the mathematical logic that tries to solve any problem statements that are neither true or false. It can also be termed as a method of decision making that resembles human reasoning. To humans, it involves all the possibilities of having a yes or no response to a particular situation. A computer or laptop can possess a conventional logic block which can produce input and a definite output as true or false. To human beings, it’s the Yes or No situation. Lotti Zadeh invented fuzzy logic I 1965 and stated that the rational decision making ranges of possibilities between Yes or No is as follows:

Fuzzy logic handles all the stages of possibilities of input to attain the exact output. It is imposed on software, hardware, or maybe a combination of both. Fuzzy logic can be applied in sectors such as; medical sector, industrial sector, finance sectors, and automotive sectors.

As we know, fuzzy logic is employed to handle the concept of partial truth. Here, the truth value may range between entirely accurate and completely false. Fuzzy logic is intimately familiar to decision theory, which is the study of choices, bringing together statistics, philosophy, mathematics, and psychology to analyze the decision-making process. Many scientists dictate that probability is a sub-theory of fuzzy logic. However, probability can model control system variables and that the success of fuzzy logic is upon this modeling. Similar to fuzzy logic, decision theory relies on the possibility and use of the tree diagram, as shown below:

Road Trip  60%

Good Weather 30%                                                       

No Road Trip  40%

                                         Road Trip  20%

Bad Weather 70%

No Road Trip  80%

In this diagram, we can illustrate that on this day;

• There is a probability of 0.3 of having good Weather and 0.7 prospects of having bad Weather.
• If the day has good Weather, there is 0.6 probability of going on a road trip while 0.4 prospects of no road trip.
• If the day has bad Weather, there is 0.2 probability of going on a road trip while 0.8 possibilities of no road trip.

Medical Application

Fuzzy logic is the main component when medical decisions are made. Since the healthcare and medical data can be critical decision making, applications in this sector have a significant benefit by using fuzzy logic-based decisions. An area that requires these skills is the computer-aided-diagnosis(CAD) in medicine. CAD can be implemented in the computer hardware and software, which can be of great help to physicians in their diagnostic decision-making. For example, when a physician finds an injury in which he or she cannot understand it but still at the incubation stage of the infection, he or she may CAD approach to classify the wound and treat its nature. The use of fuzzy logic can correctly describe the main elements that lead to injury. Fuzzy logic can be used in many areas concerning the CAD framework. This includes; biomedical signal analysis, surgical image analysis, radiology diagnosis, prostate cancer diagnosis, and diabetes diagnosis. The confusion in the medical sector is how important the information can be sourced when using fuzzy logic. The biggest problem is how to source the required fuzzy information. This is more troublesome when one has to extract such data from humans e.g., mostly patients. In this sector, what cannot be achieved in the medical diagnosis itself is a fuzzy one. How to extract fuzzy information and how to evaluate the level of accuracy of the retrieved data is still a struggle in the relation of the application of fuzzy logic. The problem of assessing the quality of the retrieved information remains a considerable disadvantage of using fuzzy logic. It is an essential feature in the CAD application area and requires more research to acquire its full potential in the decision-making process.

Industrial Application

According to the fuzzy logic applied to the industries, the first step towards the decision-making process is identifying the problem or opportunity that is at hand and requires the company’s stand out about the situation. A company’s problem can be explained as anything, not running according to the rule, plan, or standard. Problem formulation, this is the identification of the problem. It is always risky to solve the wrong question. In problem formulation, it is advisable to relate the problem with that of the past is quite useful. An example of such is high harassment cases for the present month by a supervisor among the company’s workers. In this case, the manager is supposed to make a drastic decision on whether the allegations about the supervisor are true or false and if it is true what will be his or her final judgment about the matter. Opportunity-seeking, on the other hand, is chances that might bring profits to the company when risked in taking. A perfect example of identification of opportunity is a marketing director who finds out that three of his closest competitors declare bankruptcy and will shut down operations in the next two months, this is an added advantage to him or her because after the closure he or she will be able to sell more in the market. Hence it is their responsibility to take a business risk and add more stock of the goods sold out. This is because of the expected rise of the high demand for the commodity.

There are three types of decision that industries undertake:

• Cross-cutting decisions, these are highly risky decisions made. The final decisions are made after having interconnected decisions that are made by different groups finalize the decision process.
• Bit-bet decisions are risky decisions that have a high potential to shape the future of the industry. This is not made regularly.
• Delegated decisions are low-risk decisions and can be handled by a single or work team with little suggestions from others.

Financial Application

Fuzzy logic is useful in the finance sector in the following areas:

• One can predict the future outcome of the company after a specific decision or managerial choice.
• One can forecast and avoid financial errors or mistakes.
• It gives more convenient options when venturing into investment.
• It controls the transfer of banknotes.
• It funds management.

An example of financial application; a local county government would like to venture or support the textile industry within its borders. This county has 36 textile industries that deal with both domestic and imported clothes before manufacturing and making high sales. Out of the 36, only 12 companies manufacture and produce local clothes while 24 companies import clothes, before production. Out of the 12 companies, only 11 companies target the young generation of the county. Within the 24 companies that import clothes, 21 of them focus on a young age while three targets the adults.

This can be well illustrated below:

ADULTS

LOCAL PRODUCTION

                                                                                                                                                                                                                                                       _{YOUNG GENERATION}

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          ADULTS

     _{IMPORTED PRODUCTION}                                                                                                                                                                

_{YOUNG GENERATION}

Outcome:

1. Local production and adults;

= ¹¹/₁₂ * ¹/₃ = ¹¹/₃₆

= ¹¹/₃₆ *100

= 30.56%

1. Local production and young generation;

= ¹/₁₂ * ¹/₃ = ¹/₃₆

= ¹/₃₆ *100

= 2.76%

• Imported textile and adults;

= ³/₂₄ * ²/₃ = ¹/₁₂

= ¹/₁₂ *100

= 8.33%

1. Imported textile and young generation;

= ²¹/₂₄ * ²/₃ = ⁷/₁₂

= ⁷/₁₂ *100

= 58.33%

With this kind of illustration and study, if the county government gives a higher priority to:

• The companies that import textiles and target the young generation.
• The textile companies which produce local clothes for the adults in the county.

This is because these two categories have a higher probability of earning more profits excel in the textile industry within the county.

Automotive Applications

This industry mostly deals with cars and car parts. A massive collection of data, advanced statistics, and intelligent algorithms help the automotive sector gain more knowledge and understand fuzzy logic. The management will be able to make firm decisions concerning the situations they face.

In the automotive sector, fuzzy logic is used in the following areas;

• Traffic control – gives a person the correct decision to obey the traffic rules.
• Improves automatic transmissions in the industry.
• Incerptions of intelligent highway systems.
• It shifts the scheduling technique for an automatic transmission.

Medical applications, industrial applications, automotive industries, and the finance sector are among the many areas fuzzy logic aid In administrating the enterprises gain benefits whereby they make profits; this is after making substantial business deals. In the medical sector, it enhances the new medics knowledge; this is after them interacting and coming up with a compressive decision. Before making the final decision in a particular situation, one is supposed to outline possible solutions. That can curb the problem experience, and among the possible solutions, one is picked to resolve the situation. Since the fuzzy system output is an outcome of its inputs, hence a fuzzy logic system can be useful when the input values are not available.