You are here

Autoassociation

Get the entire book!
Introduction to Neural Networks with Java

Autoassociation is a means by which a neural network communicates that it does recognize the pattern that was presented to the network. A neural network that supports autoassociation will pass a pattern directly from its input neurons to the output neurons. No change occurs; to the causal observer it appears as if no work has taken place.

Consider an example. You have an image that you think might be of a traffic light. You would like the neural network to attempt to recognize it. To do this you present the image of the traffic light to the input neurons of the neural network. If the neural network recognizes the traffic light, the output neurons present the traffic light exactly as the input neurons showed it. It does not matter which traffic light is presented. If the neural network, which was trained to recognize traffic lights, identifies it as a traffic light the outputs are the same as the inputs. Figure 2.4 illustrates this process. It does not matter what input pattern is presented. If the presented input pattern is recognized as a traffic light, the outputs will be the same as the inputs. Figure 2.4 shows two different traffic lights, the neural network allows both to pass through, since both are recognized.

Figure 2.4: A Successful Recognition
A Successful Recognition

If successful pattern recognition causes an autoassociative neural network to simply pass the input neurons to the output neurons, you may be wondering how it communicates failure. Failed pattern recognition results in anything but the input neurons passing directly to the output neurons. If the pattern recognition fails, some other pattern will be presented to the output neurons. The makeup of that pattern is insignificant. It only matters that the output pattern does not match the input pattern, therefore the recognition failed. Often the output pattern will be some distortion of the input pattern. Figure 2.5 shows what happens when the letter “B” is presented to a autoassociative neural network which is designed to recognize the letter A.

Figure 2.5: A Failed Recognition
A Failed Recognition

Technology: 

Theme by Danetsoft and Danang Probo Sayekti inspired by Maksimer