# An Introduction to Practical Neural Networks and Genetic by Christopher MacLeod

By Christopher MacLeod

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Extra resources for An Introduction to Practical Neural Networks and Genetic Algorithms For Engineers and Scientists

Sample text

However, the format of the weights and inputs are rather critical in all of them and this makes that network rather sensitive to its setup. 2 Operation in more depth Let us consider what the network is doing in more depth. 1, there are two inputs. 3. 3, inputs shown on a graph. { Vector of length L { Input 1 Input 2 The length of the vector L is (by Pythagoras): (input1) 2 + (input 2) 2 . 4. 4, the weight vector plotted on the same graph. { Weight vector length W Input vector length L { Weight 1 Weight 2 When the activity of each neuron is calculated (input1 x weight1 + input2 x weight2) what we are actually doing is calculating the vector dot product between the weight and input.

Likewise, the same can be said of neurons 5, 6 and 7 and they are combined to give region a. Finally regions a and b or combined by neuron i (an OR function in this case) so that an input in either region will give an output of 1. 4, a three layer network. 1 2 A 3 1 A 3 b 5 a 4 6 a i 4 2 b 7 B B 5 6 7 39 Out If we were to increase the number of neurons in the first layer we could increase the separators in the system, increasing the number layer two neurons increases the number of separate regions.

One thing which neural nets are particularly good at is untangling complex interrelated inputs like this. The user doesn’t have to understand the ins and outs of how the system works, but just have some examples to train the network with. During the learning process, the network will train itself to make sense of the complex relationships between inputs and outputs. Such systems are very useful in monitoring machines for potential fault conditions so that they can be identified before they result in catastrophic breakdown.