Klasifikace vzorů pomocí fuzzy neuronových sítí

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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.

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.........................50 LIST INSERTS .................................46 REFERENCES ......................................1 Network parameters ...1 Real brains....1 Learning algorithm ANFIS ....................40 8..................... CONCLUSION.....2 Simulation results .............. FUZZY SYSTEMS ...........1 Description the backpropagation algorithm.............1 The algorithm .............40 8.........................................51 ..........1..................1 2......43 8....................................................................................................................2 2.............12 3.....3 Stop the training ..32 8..........................37 8..........................48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES ....26 6..........................................................................................................9 3.......................4 2..............................1....................... NEURAL NETWORKS........................................................................................1.....................................................TABLE CONTENTS LIST FIGURES LIST TABLES 1...................................................................... THE SIMULATION ....................22 5..................................................................................................................................................................2................................2 Running the simulation ........2 The ANFIS network..................... BACKPROPAGATION ALGORITHM ......17 4................2 2..........................................................................................................................................................................................................................................................................................................1 Division into frames and preprocessing ........................43 9.............................................43 8...............................................................................2 Running the algorithm ...........40 8.........17 5.29 6.....3 Learning ...................................................................................................................................................1 Network parameters .....................................................................................1 Forward pass........................5 2........................................................................................2 Analysis using filter banks ....22 5.................15 4.......................1.................. INTRODUCTION ......................................14 3.................................................................................... THE SPEECH SIGNAL .............................................28 6...................................................................1 The 'NNV' network.. ADAPTIVE NEURO FUZZY INFERENCE SYSTEM...........................................................30 7.................................1 Signal preparation ...............................................................4............24 5..............................................1 Fuzzy Neural Networks .......1 The basic Artificial Neuron.........................................................................................................................2 Backward pass .....................9 3......................................................... THE REALIZATION THE PROGRAM............................1.................................................2 Operation neurons............................................................1..........................5 2......................................................................................................................................................................29 6..............................................................1..6 3............................................................4 Artificial neural networks .......2..................................................

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