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Kategorie: Diplomové, bakalářské práce |
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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.
Typical marking the bias the kth
neuron (Yk) the output layer w0k and
typical marking bias the jth
neuron (Zj) the inside layer v0j. This chapter will deal with the algorithm called
backpropagation.9
3..g.. Output neurons (neurons
are marked Yk, 1,. jth
neuron) matches weighted value the assigned connection between the given and
fictional neuron, whose activation always From the displayed picture then ensue,
that multilayer neural network created minimally three layers neurons: input,
output and least one inside layer.,p) shown Figure 3. These networks are: backpropagation, Hopfield
networks, competitive networks and networks using spiky neurons.1 The algorithm
Probably the most common way connect neurons with sigmoid activation
function are multilayer nets.
Many hundreds neural network types have been suggested over the years;
however, there are only small group widely uses, so-called “classic” networks, on
which many others are based.. Multilayer neural network with one inner neural layer
(neurons are marked Zj, 1,.1 Neural network with one inner neural layer ([9])
. Bias (e. 3.
Fig. BACKPROPAGATION ALGORITHM
After overviewing the basics neural networks the previous chapters, let´s
have look some practical networks, their applications and how they are trained.1..,m).
3. There are even
more variations these themes. Neurons output and inside layers must have defined
bias. Between two neighbour layers can always be
found called complete neural connection, each neuron lower layer is
connected with each neurons higher layer
