Lecture30-4-15-2002

Lecture30-4-15-2002 - MAE 552 Heuristic Optimization...

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MAE 552 Heuristic Optimization Instructor: John Eddy Lecture #30 4/15/02 Neural Networks
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Neural Networks Non-Linearity: (In response to a question asked) 1 st – We’ll define a non-linear system as one in which the outputs are not proportional to the inputs. NN’s incorporate non-linearity at each neuron by means of the neurons activation function which is typically a sigmoid. (sigmoid meaning curved like the letter C or curved like the letter S). More on this later.
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Neural Networks Modeling of a Neuron.
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Elements of a Neuron Model 1 - Synapses or Connecting links : it is at these sites that input enters the neuron. Each synapse has an associated weight. This weight will multiply the input prior to entering the summing junction (to be discussed next).
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2 – An adder (linear combiner) for summing the input signals (which have been weighted by their respective synapses prior to entering). This is our summing junction . So we have as output from our adder:
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This note was uploaded on 07/09/2011 for the course MAE 522 taught by Professor Hacker during the Spring '10 term at SUNY Buffalo.

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Lecture30-4-15-2002 - MAE 552 Heuristic Optimization...

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