Source Code Fuzzy Logic Arduino Download Step 1

Fuzzy sets of input variables and output variable with membership functions are presented in pictures above.

What bothers me is that most provided solutions are not offering intelligent solutions.

One of many things to control in smart home is atmospheric comfort.

Atmospheric comfort is defined with temperature and relative humidity - temperature ranges from 20C to 25C and relative humidity is around 50, and it varies across the globe.

But how can we measure comfort We can measure temperature and relative humidity and then we predict comfort.

Ok. But how can we automate this process We could use mathematical model to calculate it.

Believe me, you dont want to go that way, since modeling is pain in the ass There is simpler solution: artificial intelligence.

Soft sensor can be any sensor that can indirectly measure certain value.

Since we want to use artificial intelligence, we have two plausible solutions in our case: using fuzzy logic or using neural networks.

Because we do not poses tons of data, well be implementing soft sensor using fuzzy logic.

This Instructable can be used as short introduction into fuzzy logic and it shows that artificial intelligence is not necessarily something to avoid or be feared of.

Note: Instructable is not meant for beginners but for intermediate and advanced users.

Source Code Fuzzy Logic Arduino Download Step 1

Add Tip Ask Question Comment Download Step 1: What Youll Need We will need: - Arduino UNO (any other will do as well) - DHT11 temperature and humidity sensor - wires Add Tip Ask Question Comment Download Step 2: Short on Fuzzy Logic For beginning I have to write something about fuzzy logic.

Variables in digital (Boolean) logic can be TRUTH or FALSE or 1 and 0 respectively.

So there are only two values: in the area (comfortable) or outside of certain area (uncomfortable).

So solution to our problem with digital logic can provide us only with border values.

But what if we could reach any value between 1 and 0 Yes my friends, fuzzy logic can do just that There are three stages in fuzzy logic: fuzzification, prediction and defuzzification.

Fuzzification is process with which we calculate affiliation of input variable to a certain fuzzy populace or membership function.

There can be many different shapes of membership functions: trapeze, triangle, Gauss, In prediction stage we are using pre-determined set of rules (those are set by designer) to predict what the output can be or to which membership function output variable affiliate whit what level.

Predictions is what makes fuzzy logic very useful when mathematical connections between variables cannot be determined numerically or algebraically.

There are several fuzzy systems available for us to use: - Mamdani system (linguistic output variables, defuzzification can be complex) - Sugeno system (output variables are functions or singletonconstant, more suitable for implementing on microcontrollers) Compariosn between Sugeno and Mamdani system.

Add Tip Ask Question Comment Download Step 3: Designing Soft Sensor There are many tools to help design fuzzy system.

I am using Matlab, but Python with fuzzy system library can do the trick.

We have designed Sugeno fuzzy system with singleton output variables.