asap tech

NETWORKS AND FUZZY LOGICASSIGNMENT 1QUESTION ONE [ Perceptron Dichotomiser] [ 50 marks ]Two perceptron dichotomisers are trained to recognise the following classification of six patterns x with known class membership d.d1 .jpg”>1.jpg”>, d2 .jpg”> 1.jpg”>, d3 .jpg”>.jpg”>1.jpg”>, d4 .jpg”> 1.jpg”>, d5 .jpg”>1.jpg”>, d6 .jpg”> 1.jpg”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>.gif”>1.1 The first dichotomiser is a discrete perceptron as shown in Figure 1.1. Assign-1 to all augmented inputs. For the training task of this dichotomiser, the fixed correction rule is used, with an arbitrary selection of learning constant 0.05 and the initial weight vector0.09760.8632 w10.32960.3111 0.2162Assume that the above training set may need to be recycled if necessary, calculate the final weight vector. Show that this weight vector provides the correct classification of the entire training set. Plot the pattern error curve and the cycle error curve for 10 cycles (60 steps).[ 25 marks ]1.2 The second dichotomiser is a continuous perceptron with a bipolar logisticall augmented inputs. For the training task of this dichotomiser, the delta training rule is used with an arbitrary selection of learning constant 0.5with the same initial weight vector w1 in Question 1.1.1Assuming that the above training set may need to be recycled if necessary,calculate the weight vector w7 after one cycle and the weight vector w301 after 50 cycles. Obtain the cycle error at the end of each cycle and plot thecycle error curve. How would the weight vectors w7 and w301 classify the entire training set? Discuss your results.[ 25 marks ]Note:The following formulae may be used to calculate the pattern error curve andthe cycle error curve. There are 6 patterns in this question, i.e.P=6..gif”>.gif”>Figure 1.1 Discrete Perceptron Classifier Training.gif”>.gif”>Figure 1.2 Continuous Perceptron Classifier Training2QUESTION TWO [ 50 marks ]2.1 [Flight Simulation] [ 15 marks ]A new jet aircraft are subjected to intensive flight simulation studies before they are tested under actual flight conditions. In these studies, an important relationship is that between the mach number (percent of the speed of sound) and the altitude of the aircraft. This relationship is important to the performance of the aircraft and has a definite impact in making flight plans over populated areas. If certain mach levels are reached, breaking the sound barrier (sonic booms) can result in human discomfort and light damage to glass enclosures on the earth’s surface.Current rules of thumb establish crisp breakpoints for the conditions which cause performance changes in aircrafts, but in reality these breakpoints are fuzzy, because other atmospheric conditions such as the humidity and temperature also affect breakpoints in performance. For this problem, suppose the flight test data can be characterised as “near” or “approximately” or “in the region of” the crisp database breakpoints.and define a universe of altitudes asY8350,8400,8450,8500,8550,8600,8650 m, and a fuzzy set A for the altitude “approximately 8,500 m”, where2.1.2 For another aircraft speed, sayM1 for the speed “in the region of mach 0.74” where2.1.3 For the speed “in the region of mach 0.74” wheredetermine the corresponding altitude fuzzy set A1b M1 R using the sum-product composition.[5 marks]32.2 [Laser Beam Alignment] [ 35 marks ]Fuzzy logic is used to control a two-axis mirror gimball for aligning a laser beam using a quadrant detector. Electronics sense the error in the position of the beam relative to the centre of the detector and produces two signals representing the x and y direction errors. The controller processes the error information using fuzzy logic and provides appropriate control voltages to run the motors which reposition the beam. The fuzzy logic controller for this system is shown in Figure 2.1.To represent the error input to the controller, a set of linguistic variables is chosen to represent 5 degrees of error, 3 degrees of change of error, and 5 degrees of armature voltage. Membership functions are constructed to represent the input and output values’ grades of membership as shown in Figure 2.2. The rule set in the form of “Fuzzy Associative Memories” is shown in Figure 2.3.The controller gains are assumed to be GE 1, GCE 1, GU 1.2.2.1 If the Mean of Maximum (MOM) defuzzification strategy (sum-product inference) is used with the fire strengthi of the i-th rule calculated fromEi ( e ) . CEi ( ce)calculate the defuzzified output voltages of this fuzzy controller at a particular instant. The error and the change of error at this instant aree3.20 andce0.47.[10 marks]2.2.2 If the Centre of Area (COA) defuzzification strategy (max-min inference) is used with the fire strengthi of the i-th rule calculated frommin( Ei ( e ), CEi ( ce))calculate the corresponding defuzzified output voltage at a particular instant when the error and the change of error aree3.20 andce0.47 .[25 marks].gif”>.gif”>.gif”>.gif”>.gif”>.gif”>Figure 2.1 Fuzzy logic control system4.gif”>Figure 2.2 Membership functions of a laser beam alignment system.gif”>Figure 2.3 Fuzzy Associative MemoriesH. T. NguyenMarch 20145