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Cash Register Game
A contestant in
The Price is Right
is sometimes asked to play the
Cash Register Game
. A few products are described, their prices are unknown to the contestant, and the contestant has to declare how many units of each item he or she would like to (
to) buy. If the total purchase does not exceed the amount specified, the contestant wins a special prize. After the contestant announces how many items of a particular product he or she wants, the price of that product is revealed, and it is rung up on the cash register. The contestant must be careful, in this case, that the total does not exceed some nominal value, to earn the associated prize. We can now cast the whole operation of this game, in terms of a neural network, called a
Consider each product on the shelf to be a neuron in the input layer, with its input being the unit price of that product. The cash register is the single neuron in the output layer. The only connections in the network are between each of the neurons (products displayed on the shelf) in the input layer and the output neuron (the cash register). This arrangement is usually referred to as a
, the cash register in this case, being an
in neural network terminology. The contestant actually determines these connections, because when the contestant says he or she wants, say five, of a specific product, the contestant is thereby assigning a weight of 5 to the connection between that product and the cash register. The total bill for the purchases by the contestant is nothing but the weighted sum of the unit prices of the different products
. For those items the contestant does not choose to purchase, the implicit weight assigned is 0. The application of the dollar limit to the bill is just the application of a threshold, except that the threshold value should not be exceeded for the outcome from this network to favor the contestant, winning him or her a good prize. In a Perceptron, the way the threshold works is that an output neuron is supposed to fire if its activation value
the threshold value.
The weights used on the connections between different
have much significance in the working of the neural network and the characterization of a network. The following actions are possible in a neural network:
Start with one set of weights and run the network. (NO TRAINING)
Start with one set of weights, run the network, and modify some or all the weights, and run the network again with the new set of weights. Repeat this process until some predetermined goal is met. (TRAINING)
Since the output(s) may not be what is expected, the weights may need to be
. Some rule then needs to be used to determine how to alter the weights. There should also be a criterion to specify when the process of successive modification of weights ceases. This process of changing the weights, or rather, updating the weights, is called
. A network in which learning is employed is said to be subjected to
. Training is an external process or regimen. Learning is the desired process that takes place internal to the network.
If you wish to train a network so it can recognize or identify some predetermined patterns, or evaluate some function values for given arguments, it would be important to have information fed back from the output neurons to neurons in some layer before that, to enable further processing and adjustment of weights on the connections. Such feedback can be to the input layer or a layer between the input layer and the output layer, sometimes labeled the
. What is fed back is usually the error in the output, modified appropriately according to some useful paradigm. The process of feedback continues through the
cycles of operation of the neural network and ceases when the training is completed.
A network can be subject to
learning. The learning would be supervised if external criteria are used and matched by the network output, and if not, the learning is unsupervised. This is one broad way to divide different neural network approaches. Unsupervised approaches are also termed
. There is more interaction between neurons, typically with feedback and intralayer connections between neurons promoting self-organization.
Supervised networks are a little more straightforward to conceptualize than unsupervised networks. You apply the inputs to the supervised network along with an expected response, much like the Pavlovian conditioned stimulus and response regimen. You mold the network with stimulus-response pairs. A stock market
may present economic data (the
) along with metrics of stock market performance (the
) to the neural network to the present and attempt to predict the future once training is complete.
You provide unsupervised networks with only stimulus. You may, for example, want an unsupervised network to correctly classify
from a conveyor belt into part
, providing an image of each part to do the classification (the stimulus). The unsupervised network in this case would act like a look-up memory that is indexed by its contents, or a
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