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Now for our 2nd example a 6 dimentional Perceptron:

This example is also from the first home work of the Short Course on Neural Networks by Dr Klinkachorn of the Computer Engineering Department at WVU. Please note the context of the problem and the problem statement has been changed to relfect the interests of this robot club.

You can read about the history of how the perceptron was developed by Dr Rosenblatt in the 1950's.

You can read about the theory of operation of a perceptron neural network.

The Basic Description

In this example we have 6 binary inputs. When we first start the network it will not how to produce the desired output so we will train it by giving examples of input and expected output.

Inputs ______________________________Desired Outputs_

-1, -1, -1, -1, -1, -1,______________+1, +1, +1, +1
-1, -1, -1, -1, -1, +1,______________+1, +1, +1, -1
-1, -1, -1, -1, +1, -1,______________+1, +1, -1, +1
-1, -1, -1, -1, +1, +1,______________+1, +1, -1, -1
-1, -1, -1, +1, -1, -1,______________+1, -1, +1, +1
-1, -1, -1, +1, +1, +1,______________+1, -1, +1, -1
-1, -1, +1, -1, -1, -1,______________+1, -1, -1, -1
-1, -1, +1, -1, -1, +1,______________-1, +1, +1, +1

Here is a diagram of the 4 neurons and how the 6 inputs are connected. Notice that each input is connected to each neuron. Also notice that each neruon is responsible for only one output.

Mathmatically speaking this set of 4 neurons makes 4 hyper planes in 6 dimentional space where each input is one axis. Each nuron has one hyper plane that is used to determine its output.

Four our purposes though we do not need to understand this as the network will train itself and resolve all this complexity on its own.

Download the example programs and code

To use the program unzip it and run p1.exe a dos program. Press the space bar to see the next step or press escape to quit. The source code is included.

p2.zip about 62k.

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