Download e-book for kindle: An Introduction to Practical Neural Networks and Genetic by Christopher MacLeod

By Christopher MacLeod

Show description

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

Best introduction books

T. W. Anderson's An Introduction to Multivariate Statistical Analysis (Wiley PDF

Perfected over 3 variants and greater than 40 years, this box- and classroom-tested reference:* makes use of the strategy of extreme chance to a wide volume to make sure average, and on occasion optimum methods. * Treats the entire uncomplicated and critical themes in multivariate data. * provides new chapters, besides a few new sections.

Read e-book online Introduction to analytical gas chromatography PDF

Masking the rules of chromatographic separation, the chromatographic strategy from a actual chemical point of view, instrumentation for acting analyses, and operational systems, this moment version bargains info wanted for the winning perform of gasoline chromatography. It includes examples of obtainable equipment, detectors, columns, desk bound stages and working stipulations.

Get Nanotechnology: An Introduction to Nanostructuring PDF

Content material: bankruptcy 1 creation (pages 1–11): bankruptcy 2 Molecular fundamentals (pages 13–31): bankruptcy three Microtechnological Foundations (pages 33–85): bankruptcy four guidance of Nanostructures (pages 87–148): bankruptcy five Nanotechnical buildings (pages 149–209): bankruptcy 6 Characterization of Nanostructures (pages 211–224): bankruptcy 7 Nanotransducers (pages 225–269): bankruptcy eight Technical Nanosystems (pages 271–282):

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

Sample text

So can we use this scanning action to help a neural network find the pattern it’s looking for? 34 The answer to this question is yes. Exactly the same thing can be done with an imagerecognising network. 3. During this process, the pattern to be recognised will eventually end up in the centre of the grid. This is not dissimilar to what our eyes do when we study a scene. One important point about training a network for this sort of task is that we have to train it to recognise “noise” or irrelevant data as well as the wanted pattern or it will give false positives.

We then add up the result (which in this case is -2). Weights which have equal indexes (like W2,2) we make zero. This is best seen through a more complete example. 1 shows a three neuron network, trained to reconstruct three patterns. 1: A three neuron network trained with three patterns. 1 w1,1 w1,2 OA w2,1 w1,3 w3,1 w2,2 2 w2,3 OB w3,2 3 w3,3 OC Let’s say we’d like to train three patterns: Pattern number one: OA(1) = -1 OB(1) = -1 OC(1) = 1 Pattern number two: OA(2) = 1 OB(2) = -1 OC(2) = -1 Pattern number three: OA(3) = -1 OB(3) = 1 OC(3) = 1 w1,1 = 0 w1,2 = OA(1) × OB(1) + OA(2) × OB(2) + OA(3) × OB(3) = (-1) × (-1) + 1 × (-1) + (-1) × 1 = w1,3 = OA(1) × OC(1) + OA(2) × OC(2) + OA(3) × OC(3) = (-1) × 1 + 1 × (-1) + (-1) × 1 = -1 -3 w2,2 = 0 w2,1 = OB(1) × OA(1) + OB(2) × OA(2) + OB(3) × OA(3) = (-1) × (-1) + (-1) × 1 + 1 × (-1) = w2,3 = OB(1) × OC(1) + OB(2) × OC(2) + OB(3) × OC(3) = (-1) × 1 + (-1) × (-1) + 1 × 1 = -1 1 w3,3 = 0 w3,1 = OC(1) × OA(1) + OC(2) × OA(2) + OC(3) × OA(3) = 1 × (-1) + (-1) × 1 + 1 × (-1) = w3,2 = OC(1) × OB(1) + OC(2) × OB(2) + OC(3) × OB(3) = 1 × (-1) + (-1) × (-1) + 1 × 1 = -3 1 47 And here’s one to try.

When scan reaches the end of a row, it moves down one pixel and starts moving back to the left Network scans image from top left. Moving to the right one pixel at a time Scan ends when it has reached bottom right. So much for centring the pattern, what about making it the correct size? Well, this can be done by pre-processing as part of the same process. 4. 4, image sizing. Image information is reduced to correct size for network inputs by pixel averaging or similar method. Network input starts as whole image size When one size has scanned the image, network input area reduces in size Input area keeps reducing and scanning until it is smallest practical size.

Download PDF sample

An Introduction to Practical Neural Networks and Genetic Algorithms For Engineers and Scientists by Christopher MacLeod


by Kenneth
4.4

Rated 4.62 of 5 – based on 32 votes