Deep Learning and Neural Networks in Automation

1.0 Overview

With the need to manage building resources, Building Automation and systems integration has become the norm in large industrial buildings, campuses, skyrises and it is slowly creeping into the consumer housing market. The technology of connecting edge devices to systems and then systems to the cloud has advanced dramatically and now the need to manage and analyse data from hundreds or thousands of devices within many networks is further pushing innovation in the industry. Data is often referred to as the oil of the 21st century, in which case, Advanced Data Processing/Machine Learning/AI can be considered the refineries of the 21st century.

Today, we want to look at Deep Learning, a subset of machine learning, and Neural Networks in the Automation industry. Deep learning is a method of processing data and creating patterns to make decisions that imitates a living brain. The theory is that with deep learning, a computer is able to train itself to process and learn from data. Neural Networks are a set of algorithms designed to recognize patterns in a way that mimics the neural networks of human/animal brains. Deep learning uses Neural Networks stacking them up on top of each other to make much more complex entities to produce an output for a given input. Thus, the relationship between Deep Learning and Neural Networks is that Neural Networks is the structure that deep learning uses to make its decisions. Without Neural Networks there would be no deep learning.

The functional difference between Neural Networks and Deep Learning is that Neural Networks need to be trained while Deep Learning can extend its training by itself, it is capable of self-training and learning. From a practical stand point, Neural Networks are provided as a tool in some Automation PLC's and Scada, VFD and other controllers whereas if you wanted to employ Deep Learning, you would need an Amazon Size computer to make it work. In other words, Deep Learning is not available as an automation technology... Yet.

On the other hand, Neural Networks are employed in the automation industry. For the rest of the article, we will look at Neural Networks in the automation Industry.

How do Neural Networks work?

To explain how they work we need to draw a whole number of block diagrams and truth tables. Then you need to apply your mind in a very concentrated way and follow the detail of the logic. We will leave this for later. We don't want you to stop reading this article yet. If you want a simple idea read the section of this article called Truth Tables.

Imagine a series of Boolean logic gates connected in a system – AND, NOT, OR, XOR etc. We will call this system a Artificial Neuron. Training the Neuron means building a truth partial truth table. For a given set of inputs you teach it what output you require. Then you train it with a different set of inputs and teach it what output you like. You are starting to teach the Neuron what its truth table should be. What you do not do, is train it for every possible set of inputs.

An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another.

Now, if you design the Artificial Neuron well, when it is presented with something you did not train it to recognize, it will set the output to the least wrong output or expressed another way to the output it thinks is best for the partial match on the inputs.

(2)The least wrong output can still be wrong

If the least wrong output can be wrong on needs to be concerned about human safety and equipment protection. Normally they are applied in very limited contexts – ie to provide control under very average operating conditions. The outputs from neural Networks should not be applied at extreme ranges of the normal operating condition or in situations where tiny changes in output can produce huge multiplier effects.

Since a neural network is essentially a black box you don't know how it arrived at its answer. For example. If you designed and trained a neural network to recognize a smiley face in a photo then you have no idea if the network has simply remembered the training images that had smiley faces or whether it actually recognizes the smiley face. Read more here.

(4) They are nothing like how a human brain works – not even a distant approximation

Human brains can learn. They are adaptive. Their design is completely different. Human neurons typically have more feedback connections than feed forward connections indicating that the human algorithm is far more of a feedback system than feed forward. Artificial networks generally only have the feedback system active during training. Human brains work by memorizing the invariant form. Ie they can abstract structure from a pattern. Artificial networks cannot.

If you hear someone sing "happy birthday to you.." you recognize the melody irrespective of what key it is sung in. Your brain has stored the invariant form. The change of frequency and interval from one note to another. Your brain takes the invariant memory and combines (feedback) it with the actual input to predict the actual next note you will hear. If you could design an artificial neural network to recognize the song you would have to teach it the song in all keys.

Read about deep learning here

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