Consider the following neural network which takes two binary-valued inputs and outputs. Which logical function does it approximately compute?

Draw and write out the transition functions for a 2-tape Turing machine that recognizes the language {w#w|w ∈ {0, 1} ∗ }.

Trace the Perceptron algorithm for the following input. Suppose the actual labels are with respect to a line passing through the origin with normal vector w* = (-1, 1); for example, the true label of (1,0) is '-1' and that of (0,1) is '+1'. The points are ₁ = (0, -1), x2 = (-1,0), x3 = (2,0), x4 = (√√2,1), x5 = (-3,-2). You need to follow the same assumptions as in the example in Slide 6 of Module 10. Show your work and specify the number of mistakes by the algorithm. x5: (-3,-2) 24=(√2,1) 22: (-1,0) xy: (2,0) (0,-1)

۲/۱ العنوان оне برات A- Consider a fuzzy logic controller is used to control the speed of a motor by changing its input voltage (V) according to two input variables; speed (SP), and speed change rate SC. Let the fuzzy set of SP be {Slow (S), Normal (N), Fast (F)), and the fuzzy set for SC be (Low (L), Medium (M), High (H)), and for the control action be {Slow (SCE [0, Down (DN), No Change (NC), Speed Up (Up)}, where, (SPE [250, 1000]), 10]), and (Ve [3, 8]) with step = 0.5. Find the control action if SP=910 And SC= 6.5 based on the fuzzy rules shown in table ^ S N F L Up NC NC M Up NC NC HNC DN DN Find th