WEBVTT Kind: captions Language: en 00:00:14.240 --> 00:00:22.440 Now Let’s go a little deeper applying Fuzzy Set Theory to Fuzzy Logic Controller. 00:00:23.820 --> 00:00:31.200 It can be seen here, the fuzz logic controller is constructed of three parts. 00:00:32.220 --> 00:00:37.480 They are “Fuzzifier”, “Inference Engine” and “Defuzzifier”. 00:00:38.680 --> 00:00:44.000 When a crisp input goes into the fuzzy logic controller, 00:00:44.840 --> 00:00:54.320 it is firstly mapped to fuzzy set to determine the membership degrees of fuzzy sets by the fuzzier. 00:00:55.420 --> 00:01:02.620 Then, it goes to “inference engine” where the experts’ knowledge is placed. 00:01:03.220 --> 00:01:10.500 The experts’ knowledge is described by the fuzzy rules that are if-then rules. 00:01:11.260 --> 00:01:13.920 Finally, through Defuzzifier, 00:01:15.180 --> 00:01:19.480 a crisp command is made to control the plant. 00:01:19.920 --> 00:01:24.420 Maybe the fuzzy logic controller is still fuzzy for you. 00:01:24.820 --> 00:01:28.000 So I am going to show an design example 00:01:28.120 --> 00:01:33.740 to help you to understand fuzzy logic controller more clearly. 00:01:34.420 --> 00:01:39.360 The purpose of the example is to design the fuzzy controller 00:01:40.000 --> 00:01:46.880 to control the air-conditioner such that the room temperature maintain suitable. 00:01:47.380 --> 00:01:51.900 The input of the fuzzy controller is the room temperature. 00:01:52.700 --> 00:01:59.920 The output of the fuzzy controller is the Command of Heating, Maintaining or Cooling. 00:02:00.800 --> 00:02:04.240 At first, we have to design the fuzzifier. 00:02:04.640 --> 00:02:08.260 We use the fuzzy sets shown in this figure. 00:02:09.020 --> 00:02:13.200 Assume that the room temperature is 21 degree. 00:02:13.800 --> 00:02:20.320 Hence the membership degree in cold is 0.75 00:02:20.880 --> 00:02:24.800 and in suitable is 0.25. 00:02:25.420 --> 00:02:28.460 Then we can go to the Inference Engine. 00:02:28.920 --> 00:02:35.560 The inference engine is where the Experts’ experience placed. 00:02:36.360 --> 00:02:41.640 We can easily complete the fuzzy rules in the inference engine. 00:02:42.400 --> 00:02:49.440 Rule 1: If the room temperature is Hot, then take the action of Cooling. 00:02:50.360 --> 00:02:51.020 Rule 2 00:02:51.800 --> 00:02:54.560 If the room temperature is Comfortable, 00:02:55.260 --> 00:02:58.080 then take the action of Maintaining. 00:02:58.860 --> 00:02:59.660 Rule 3 00:03:00.540 --> 00:03:03.520 If the room temperature is Cold, 00:03:03.980 --> 00:03:06.860 then take the action of Heating. 00:03:07.980 --> 00:03:10.800 Finally Defuzzifier can determine: 00:03:11.560 --> 00:03:17.980 What percent of effort it would take to heat up the room temperature. 00:03:18.900 --> 00:03:24.520 What percent of effort it would take to cool down the room temperature. 00:03:24.980 --> 00:03:27.880 or To maintain the room temperature. 00:03:28.560 --> 00:03:32.780 In the case of 21 degree room temperature, 00:03:33.440 --> 00:03:41.400 the membership degree in cold is 0.75 and in suitable is 0.25. 00:03:42.120 --> 00:03:45.300 According to rule 2 and rule 3, 00:03:45.580 --> 00:03:47.820 the fuzzy logic controller 00:03:48.240 --> 00:03:54.760 will give a command of heating at 75 percent effort. 00:03:55.300 --> 00:04:02.780 Finally we can see some Applications of Fuzzy Set Theory and Fuzzy Logic Controller. 00:04:03.740 --> 00:04:08.260 The fuzzy set theory can be used for clustering. 00:04:08.660 --> 00:04:12.800 This is a grey scale image of human brain. 00:04:13.400 --> 00:04:22.200 We can use fuzzy clustering approach for image segmentation to help doctors diagnose. 00:04:22.780 --> 00:04:29.100 The fuzzy logic controller can be used to automatic control systems, 00:04:29.580 --> 00:04:30.480 for example, 00:04:30.860 --> 00:04:33.320 autonomous vehicle systems. 00:04:34.160 --> 00:04:38.280 Also we can use the concept of Fuzzy Set Theory 00:04:38.740 --> 00:04:48.160 to help us make decision in various areas such as in finance and management.