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

By Christopher MacLeod

Read Online or Download An Introduction to Practical Neural Networks and Genetic Algorithms For Engineers and Scientists PDF

Similar introduction books

The Muqaddimah: An Introduction to History

The Muqaddimah, usually translated as "Introduction" or "Prolegomenon," is an important Islamic historical past of the premodern global. Written through the nice fourteenth-century Arab pupil Ibn Khaldûn (d. 1406), this huge paintings laid down the principles of numerous fields of information, together with philosophy of background, sociology, ethnography, and economics.

The Evaluation and Optimization of Trading Strategies

A newly improved and up-to-date variation of the buying and selling vintage, layout, checking out, and Optimization of buying and selling structures buying and selling platforms professional Robert Pardo is again, and in The evaluate and Optimization of buying and selling options , a completely revised and up to date version of his vintage textual content layout, trying out, and Optimization of buying and selling platforms , he finds how he has perfected the programming and checking out of buying and selling platforms utilizing a profitable battery of his personal time-proven strategies.

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

Example text

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.

714 Now let’s calculate the length of the new weight vector (the training formula doesn’t preserve length). 79 57 Worked example (continued). Finally then let’s plot a graph showing what’s happened: Old weight vector New vector We can see that the weight vector has moved towards the input. Obviously then, this network requires a little more thought to set up compared to some of the others. The weights and inputs need to be processed so that they are all vectors of length one. The network will also work better if the weights are equally distributed around the unit circle.

Since the network is performing a mapping function, it can also be trained to process the waveforms and implement mathematical transforms on them to form Digital Signal Processing (DSP) functions3. This example also illustrates a problem which we will return to in the next chapterthat of the presentation of data to the network. The network will only recognise the waveform if it is the same size as the data the network was trained form. If it has speeded up or slowed down then the network won’t “see” it as the same pattern.