IEEE Power & Energy Magazine - May/June 2022 - 35

approximates the " true " trajectory (dashed blue line). A good
neural network will do so accurately.
Classification and regression neural networks have the
same structure (see the right side of Figure 2). Neurons are
organized in layers, with each layer connected in series
with each other. Each neuron contains a nonlinear activation
function. The neurons in each layer are connected
through linear functions with some or all of the neurons of
the previous layer and the next layer. In this simple structure,
the linear functions contain parameters we can adjust,
Classification
Possible Inputs
whereas the nonlinear activation function in each neuron
remains unchanged.
In many neural network implementations, neurons act
as a switch, either allowing the signal to pass or restricting
it from doing so. Because of these nonlinear activation
functions, neural networks can represent very complex, and
sometimes not even explainable, nonlinear processes. The
magnification of a single neuron in Figure 2 shows one
option for how such a nonlinear function can look, but there
are many more. With the appropriate selection of linear
Predicted
Classifications
Neural Network
Architecture
Layer
Neural Network
Regression
Output
In
In
In
Prediction
Out
Out
Output
Input
Neural Network
Linear
Input
Ground Truth
Input
figure 2. The basic structure of a classification and regression task, and the fundamental architecture of a simple neural
network that forms the core of either task.
Transformation
Nonlinear
Activation
Function
Data Creation
Neural Network Training
Simulating
Physical
Models
Training Data
Training
Physical
Models
Neural
Network
Testing
Physical
Models
Performance
Guarantees
Measuring
Testing Data
Additional Elements
figure 3. The classical ML procedure, and the new elements for improving the procedure.
may/june 2022
ieee power & energy magazine
35
Neural Network Testing
Statistical
Evaluation

IEEE Power & Energy Magazine - May/June 2022

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