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Create and Train a Feedforward Neural Network

This example shows how to train a feedforward neural network to predict temperature.


To run this example, you must be logged in to a MathWorks Account that is licensed to use the Deep Learning Toolbox .

Read Data from the Weather Station ThingSpeak Channel

ThingSpeak™ channel 12397 contains data from the MathWorks® weather station, located in Natick, Massachusetts. The data is collected once every minute. Fields 2, 3, 4, and 6 contain wind speed (mph), relative humidity, temperature (F), and atmospheric pressure (hg) data respectively. Read the data from channel 12397 using the thingSpeakRead function.

data = thingSpeakRead(12397,'Fields',[2 3 4 6],'DateRange',[datetime('Sep 7, 2016'),datetime('Sep 9, 2016')],...

% Assign input variables
inputs = [data.Humidity'; data.TemperatureF'; data.PressureHg'; data.WindSpeedmph']; 
% Calculate dew point from temperature and relative humidity to use as the target
% Convert temperature from Fahrenheit to Celsius
tempC = (5/9)*(data.TemperatureF-32);
% Specify the constants for water vapor (b) and barometric pressure (c)
b = 17.62;
c = 243.5;
% Calculate the intermediate value 'gamma'
gamma = log(data.Humidity/100) + b*tempC ./ (c+tempC);
% Calculate dew point in Celsius
dewPointC = c*gamma ./ (b-gamma);
% Convert to dew point in Fahrenheit
dewPointF = (dewPointC*1.8) + 32;

% Assign target values for the network
targets = dewPointF';

Create Two-Layer Feedforward Network

Use the feedforwardnet function to create a two-layer feedforward network. The network has one hidden layer with 10 neurons and an output layer.

net = feedforwardnet(10);

Train the Feedforward Network

Use the train function to train the feed-forward network.

[net,tr] = train(net,inputs,targets);

Use the Trained Model to Predict Data

After the network is trained and validated, you can use the network object to calculate the network response to any input.

output = net(inputs(:,5))
output =


See Also