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Non-contact measurement systems utilizing 3D scanning to collect data for applications such as reverse engineering and quality inspection from Perceptron. ... Perceptron Adds Four 3D Scanning; VARs for Asia Pacific Region; Find out more;
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www.perceptron.com/3dsca.html
www.perceptron.com/3dsca.html
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The perceptron is a program that learn concepts, i.e. it can learn to respond with True (1) or False (0) for inputs we present to it, by repeatedly "studying" examples presented to it. ... Our perceptron network consists of a single neuron connected to two inputs through a set of 2 weights, with an additional bias input.
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www.cs.bgu.ac.il/~omri/Perceptron/
www.cs.bgu.ac.il/~omri/Perceptron/
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Perceptron - Wikipedia, the free encyclopedia
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The perceptron is a type of artificial neural network invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt. It can be seen as the simplest kind of feedforward neural network: ...
en.wikipedia.org/wiki/Perceptron
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Using this applet, you can train the perceptron to act as a binary logic unit. It can compute or approximate most 2-input Boolean functions. However, a problem arises when trying to train the perceptron on the XOR (or XNOR) function.
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lcn.epfl.ch/tutorial/english/perceptron/html/index.html
lcn.epfl.ch/tutorial/english/perceptron/html/index.html
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Perceptron Learning Rule Applet ... This program applies the perceptron learning rule to generate a separating surface for a two class problem (classes X and O). The X's are represented by a Red Box while the O's are represented by a Purple Box.
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neuron.eng.wayne.edu/java/Perceptron/New38.html
neuron.eng.wayne.edu/java/Perceptron/New38.html
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Single Perceptron The structure of a single perceptron is very simple. There are two inputs, a bias, and an output. A simple schematic diagram is shown at the right - the two circles at the bottom are the inputs, the black square is the bias, and the circle at the top is the output.
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library.thinkquest.org/18242/perceptron.shtml
library.thinkquest.org/18242/perceptron.shtml
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This perceptron, which could learn in the Hebbean sense, through the weighting of inputs, was instrumental in the later formation of neural networks. He discussed the perceptron in his 1962 book, Principles of Neurodynamics.
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www.csulb.edu/~cwallis/artificialn/History.htm
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Functionality ...; DynaStar™ asset management and maintenance software solution is designed to optimize asset value and improve manufacturing productivity. Built on a foundation of object-oriented technology and the latest software industry standards, ... Performances ... DynaStar™ was designed with speed in mind.
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www.usnr.com/opt/dynastar/ds2000_details.htm
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The "perceptron" was the first attempt at this particular type of machine learning. It attempts to classify input signals and output a result. It does this by being given a lot of examples and attempting to classify them, and having a supervisor tell it if the classification was right or wrong.
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www.ct.infn.it/cactus/applets/Perceptron.html
www.ct.infn.it/cactus/applets/Perceptron.html
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