Saturday, May 16, 2020

Single Neuron Character Recognition Essay - 1379 Words

Nelson Mandela Metropolitan University Character Recognition – Single Neuron WRCI 411 – Assignment 1 RXXXXX XXXXXX - 2100XXXX August 2013 RXXXXX XXXXXX – s2100XXXX WRCI 411 – Assignment 1 August 2013 Contents List of Figures and Tables ........................................................................................................................ 1 Figures ................................................................................................................................................. 1 Tables .................................................................................................................................................. 1 Theory†¦show more content†¦The output is based on a function, usually a step function or sigmoid function (basically a rounded or differentiable step function). The neuron should output a 0 until it ‘fires’ when it should output a 1. This allows it to be used as a logic function. For this case the neuron should fire for only one letter/character (and an interfered equivalent) and for all other possible cases it should not fire. The strength of a neural network is that it can ‘learn’ or be ‘taught’ a pattern and recognise this pattern. From this it should be able to make decisions for cases it has not seen before. There are many methods for teaching a neuron, most relying on derivation to find the slope of the so-called weight-space (some rely on brute-force, but these are seldom used). The basic Gradient Decent rule acts on the slope of the weight-space and will be used for this report. Method Teaching The code was written in m-code and implemented by MathWorks Matlab R2011a. The artificial neuron is taught by manipulating the values of the elements in the weight matrix (W in the code) until the error in the output matrix (from the dot product of the teaching set (X) and W) is lower than a set number (0.005). This is achieved with a while loop. 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