Degree of Membership of a Value in a Fuzzy Set

I found the solution to the Woo’s problem by using the tools I mentioned above. I converted the problems constraints into inequalities and from there I was able to put all of them onto a graph and find the feasible region. Then from the prices given in the problem I was able to make my first profit line, and was able to find out the

| if maritalStatus = ‘M’ taxRate = MARRIED_RATEif maritalStatus = ‘S’ taxRate = SINGLE_RATEif maritalStatus = ‘D’ taxRate = DIVORCED_RATEif maritalStatus = ‘W’ taxRate = WIDOWED_RATEIf hoursWorked <= 40 grossPay = hoursWorked * hourlyRateElse regularPay = (40 * hourlyRate) overtimePay = ((hoursWorked-40) * (hourlyRate * 1.5)) grossPay = regularPay + overtimePaytaxAmount = grossPay * taxRatenetPay = grossPay - taxAmount

System.out.println(); System.out.println("Total Sales \t Total Compensation"); System.out.println("----------- \t ------------------"); double minimumSales = 80000; double potentialCommission = minimumSales * 0.05; double potentialCommission1 = 85000 * 0.05; double potentialCommission2 = 90000 * 0.05; double potentialCommission3 = 95000 * 0.05; double potentialCommission4 = 100000 * 0.0625; double potentialCommission5 = 105000 * 0.0625; double potentialCommission6 = 110000 * 0.0625; double potentialCommission7 = 115000 * 0.0625; double potentialCommission8 = 120000 * 0.0625; double potentialCompensation = salary + potentialCommission; double potentialCompensation1 = salary + potentialCommission1; double potentialCompensation2 = salary + potentialCommission2; double potentialCompensation3 = salary + potentialCommission3; double potentialCompensation4 = salary + potentialCommission4; double potentialCompensation5 = salary + potentialCommission5; double potentialCompensation6 = salary + potentialCommission6; double potentialCompensation7 = salary + potentialCommission7; double potentialCompensation8 = salary + potentialCommission8;

multiply the re-work cost per case by the expected proportion of under filled cases, you get the total re-work

Design an algorithm in pseudocode to solve the problem. Make sure to include steps to get each input and to report each output.

There will be many different constraints in the database. An example of a constraint is one if the students' GPA will have to be between 0.00 and 4.00. Another example would be that each class can only have one instructor. There would be a constraint on the social security number ensuring that there are only nine numbers and in the format is xxx-xx-xxxx, and each individual number would be between 0 and 9. There would be a constraint for the telephone numbers as well. Each individual number would be between 0 and 9 in the format of (xxx)-xxx-xxxx.

In my implementation of fuzzy genetic algorithm ii followed the steps of genetic algorithm so I started with reading the data of KDDCup99 and creating a population, this population was composed of number of individuals which are the records in the KDDCup99 which means that each individual has an array of genes to hold the features of audit records. This was accomplished by first encoding audit record data into binary because some feature such as protocol type has value "TCP". Once i finished creating my initial population I evaluated every individual in the population to calculate its fitness using function below

The overall learning algorithm now proceed as follows; first, propagate the input forward using equation 3.3 and equation 3.4; next, propagate the sensitivities back using equation 3.15 and equation 3.12; and lastly, update the weights and offset using equation 3.7, equation 3.8, equation 3.10 and equation 3.11. (Murphy,

In this particular case, Randy will need to assign the correct numbers to the correct category. For the purposes of this case study, assume T will equal 1 to make the equation represent one year of employment in one of the ice cream shops. For following variables, Nn will equal 50 as there will be 50 applicants total selected to be hired, rxy will represent .30 in one equation representing the interview and job performance and in the other equation, it will represent .50 which will represent the work sample predictor and job performance, SDy will be chosen to represent .20, Ẑs will be .80 because it will be the predictor score of the selected applicants, Na will represent 100, as that is the total number of applicants that submitted applications, and Cy will represent the cost per applicant in the interview and job performance in one equation as 100 and it will represent 150 in the other equation for work sample and job

A linear formula idea will be used and the decision variables will be labeled as follow:

Write a constraint to ensure that if machine 4 is used, machine 1 will not be used.

Wang Ai-zhen , Ren Guo-feng [8] They determine the wash time by observing the input variables like Turbidity and turbidity change rate. In this paper the values are obtained from , the sensor of the washing machine i.e. Turbidity and turbidity change rate which is then passed to the information processing system , to process, the information was sent them to the controller. The value of input parameters are translated into fuzzy variables by the process of fuzzification, using MCU, accordance with the fuzzy inference rules and, the result is the fuzzy value. After defuzzification the crisp value, the washing time is obtained which we modify by the concept of soft computing neural

Management has requested that the production of baseball gloves (regular model plus catcher’s model) be such that the total number of gloves produced is at least 750. That is, 1x1 + 1x2 > 750

Given consideration to all of the known information given to this evaluator and the multiple

correct state (Right Status) to the correct location (Right Place) - "6R", and to minimize the total