Fuzzy Logic in Smart Homes

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Information about Fuzzy Logic in Smart Homes

Published on March 19, 2009

Author: nicbet

Source: slideshare.net

Description

A presentation on the usage of fuzzy logic in smart homes, I prepared for a graduate course on fuzzy logic.

SMART HOMES AND FUZZY LOGIC presented by Nicolas Bettenburg 1

1,000,000 years ago 2

Photograph by Sisse Brimberg © 2007 National Geographic 250,000 years ago 3

not so long ago 4

today 5

What’s next? Latest Trend: Smart Homes 6

Imagine when you come home ... ... your front door opens on its own ... lights turn on automatically ... your fridge is filled ... the pets are already fed 7

Pioneers 8

You need: + + A home Lots of Sensors Controller + Actuators 9

Location Time Day Rules Devices 10

The system should be • context sensitive • adaptive • invisible 11

Context-sensitive • act application-specific lighting for a party • context triggered actions the cake comes in usually achieved using Machine Learning 12

Adaptive • our habits change summer vs. winter ... • different persons have different perceptions male vs. female ... usually achieved with Neural Networks 13

Capturing the Environment Time == 2pm Month == September Date == 21 Humidity == 35% Luminosity == 100 lx Location == 30.12 , 41.21, 8.51 ... we will end up with millions of rules! 14

Humans perceive their environment differently! 15

We use natural language! Time is ‘around noon‘ Date is ‘beginning of fall’ Weather is ‘still warm and dry’ Location is ‘in the bathroom’ 16

Sensors Humans vs. measure crisp use natural values language 27.14 ºC pretty warm 17

How can we solve this? 18

Use Fuzzy Logic 19

Two Obstacles (1) learn from user’s actions (2) pro-actively anticipate user’s needs 20

invisible. It has removable floor and ceiling tiles, lots of space for equipment and customized electrics, which allow us to reconfigure lights, wall sockets and Example: Lighting Control System switches as needed. A picture of the smart home is shown in figure 1. 21

Example: Lighting Control System Inputs outdoor light level person activity time Outputs ceiling light power venetian blinds position 22

Example: Lighting Control System dark normal bright 1 0 0 120 250 Outdoor Illuminance 23

Example: Lighting Control System at home absent 1 0 0 255 Person activity Sensor gives either 0 or 255 (binary) 24

Example: Lighting Control System t1 t2 t3 t4 t5 1 ... 0 -20 0 120 1440 Time 1440 minutes mapped on 50 ‘zones’ 25

Example: Lighting Control System on on off off 1 1 0 0 0 255 0 255 Ceiling Blinds Override: on/off Override: on/off 26

Example: Lighting Control System quite small quite much much small normal 1 0 250 0 250 Output 1: Ceiling Light Power Defuzzify using ‘Center of Gravity’ 27

Example: Lighting Control System down up closed up closed center 1 0 250 0 250 Output 2: Venetian Blinds Position Defuzzify using ‘Center of Gravity’ 28

event-based control. Example: Lighting Control System Table 1. An example of a rule table Example Rule Fuzzify input, map to output and defuzzify output Table 2 shows all the possible types of rules used and the possible values in the rule table with the used rules. In autonomous control, the override flags of outputs on the input side are defined to be off, marked with number one. The output states on the input side are marked with zeros, so that the state of an output is ignored during the input aggregation. All the other values of 29

Just another Mamdani-like system ... 30

... But this system can learn its rule table without prior knowledge! 31

Learning Process Data Fuzzification Data Filtering Sensors Server Rule Database Update Fuzzy control process 32

Automatic Data Gathering • Monitor Input and Output devices • Record their values periodically • Reasonable Timer: 1 minute Data Fuzzification Data Filtering Sensors Server Rule Database Update Fuzzy control process 33

Data Fuzzification • Read recorded input and output values. • Determine membership function with greatest degree of membership. • Store fuzzy value for later use in learning process. Data Fuzzification Data Filtering Sensors Server Rule Database Update Fuzzy control process 34

Data Filtering • Search most common combinations of inputs and outputs within a time period. • Time period no longer than one fuzzy time unit. Data Fuzzification Data Filtering Sensors Server Rule Database Update Fuzzy control process 35

Rule Base Updating • Search database for input combinations determined in previous step. • If not found: add rule with small weight • If found: increase/ decrease weights • If weight becomes 0: remove Data Fuzzification Data Filtering Sensors Server Rule Database Update Fuzzy control process 36

Discussion • System well suited for pro-active control • Learns behavior quickly • Needs tweaking of values and thresholds • Timer too small: data explosion • Timer too long: behavior not adaptive enough 37

Still there are many more problems to solve... Scale system up to hundreds of sensors and thousands of rules? Control Interfaces? Interaction between controller systems? 38

Research Work Covered A.Vainio et al. : Learning and adaptive fuzzy control system for smart home. H.Sunghoi et al. : Adaptive Type-2 Fuzzy Logic for Intelligent Home Environment. Minkyoung Kim et al. : Behavior Coordination Mechanism for Intelligent Home. 39

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