0.1 0.35 ZI 0.7 0.4 0.85 0.2 h 0.55 0.9 0.5 h Z2 0.4 0.2 0.34. Figure 1 Q/5 A- By using the perceptron rule learning, find the weights required to perform the following classification: vectors [1 -1 -1 1], [-1 -1 1 1] and [1 1 -1 1] are members of class with target value (-1); vector[ 1 1 1 -1] not a member of class with target value (1). Initially the weights and (e) are taken as zero 1 if yin > e Y= F(yin)= 0 if -es yin se -1 if yin <-e B- By using Genetic algorithm, find the value of (y1, y2, y3, y4, y5) to the following equation. F(x) = (7*X1 + 2.4*X2 + 5.5 *X3 + 0.8 *X4 + 6.4*X5) F(x)= 14 ८. //र. Error =1\/f(x) new - f(x) / 3

In Matlab practice matrix operations with for loops. Create a random matrix 10*10 in size, e.g. A=rand(10); Then use two for loops (nested for loops) to “survey” each element in each column and row and do the following operations: (a) Add all the elements together; (b) When the element is smaller than 0.5, set it to zeros; When the element is larger or equal to 0.5, set it to ones.

3.) Given this truth table, find the unoptimized formula this corresponds to, along with the version which was optimized with a K-Map. A B C D O 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 1 1 0 0 0 1 0 0 1 1 0 1 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 Unoptimized Sum of Products equation 3a) 0 = Draw the K-map and simplify Optimized Sum of Products equation 3b) 0 =

Use the following K-Map to create an equation "yz yz y-z Wx W-X 1, 1,