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Kategorie: Diplomové, bakalářské práce |
Tento dokument chci!
Práce popisuje základy principu funkčnosti neuronů a vytvoření umělých neuronových sítí. Je zde důkladně popsána struktura a funkce neuronů a ukázán nejpoužívanější algoritmus pro učení neuronů. Základy fuzzy logiky, včetně jejich výhod a nevýhod, jsou rovněž prezentovány. Detailněji je popsán algoritmus zpětného šíření chyb a adaptivní neuro-fuzzy inferenční systém. Tyto techniky poskytují efektivní způsoby učení neuronových sítí.
Each neuron the inner layer calculates its activation (zj) and sends
this signal all the neurons the output layer.
Formally, for the training set can consider set elements (patterns) that are
arranged pairs defined follows:
T {{S1,T1} {S2,T2}…{Sq,Tq}}
Si [s1 sn] ‹0, (3..
During feedforward signal spreading, each neuron the input layer (Xi, =
1,.g.., Zp).
.,n) receives input signal (xi) and mediates its transfer all neurons the inner
layer (Z1. This method adaptation the opposite direction the
spread information from higher layers lower layers. Each neuron the output layer
calculates its activation (yk), which matches its real output (kth
neuron) after
submission the input sample.
Another issue the ability generalization over the learned material, other
words, how the neural network able deduce the basis learned phenomea
that were not part the learning process, but can somehow deduced from the
learned. Stop first the training set. Signal spreading biological
system proceeds such way too, where input layer can created e. The question then will be, how synaptic weights leading correct
response the input signal are defined. Algorithm itself includes three periods: feedforward spreading the
input signal training pattern, backward spreading errors and actualization of
weighted values connections.
In principle this way response neural net the input stimulus can be
obtained, given excitation input layer neuron.. The process determining the synaptic
weights linked again with the concept learning the neural networks. Each
training set pattern describes, how neurons are excited the input and output layers..1)
Ti [t1 tm] ‹0, 1›
where number training set patterns
Si excitation vector the input layer consisting neurons
Ti excitation vector the output layer consisting neurons
sj, excitation the jth
neuron the input, respectively the output
layer
The method that allows the adaptation the neural network training set is
called backpropagation.
What needed for learning the neural network? both the training set
containing elements describing the solved problem and then method that can fix
these samples the form neural network synaptic weight values, including the
already mentioned ability generalize, possible. with visual
cells and the output layer the brain are then identified individual objects of
watching.10
Backpropagation algorithm used approximately 80% all neural network
applications
