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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........ THE SPEECH SIGNAL ...............................................................4 Artificial neural networks .................................................................................................................................................................................................22 5..1 Forward pass........37 8.........................1................................................................................................................1...........................................................................................................................3 Stop the training ..............26 6..................43 8.................48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES ..................................................................................................1 Description the backpropagation algorithm.............1..................................................29 6.....28 6.............1 Signal preparation ........................................................................................5 2...................................32 8...................1 Real brains...............................................................................................................................1 2......... CONCLUSION...50 LIST INSERTS ..........................2 Backward pass ..............5 2...24 5........................40 8........................2..............40 8...............2 Running the algorithm ...1.....1 Network parameters ....... THE SIMULATION ......................2 Running the simulation ..9 3........................................................1 The algorithm ....................... INTRODUCTION .........................................1 The basic Artificial Neuron..........9 3..............................TABLE CONTENTS LIST FIGURES LIST TABLES 1...........................................1.2 Analysis using filter banks ...............................1 Network parameters ..2 Operation neurons..............................2 2..................................................................................1.................................................................17 5... BACKPROPAGATION ALGORITHM ...............................................1...................................46 REFERENCES ..................... NEURAL NETWORKS..............15 4.......................................................4....4 2..................................................................................................................................................................................2 The ANFIS network..............................43 9..............................1 Fuzzy Neural Networks ..1 Learning algorithm ANFIS .............12 3.....................................................14 3....................................................................................................................................................29 6......1 The 'NNV' network..........................................................................................................................................................2 Simulation results .......................................................................................... FUZZY SYSTEMS ..............................................6 3........ THE REALIZATION THE PROGRAM..........30 7.........................................................................17 4..........................................................40 8..................... ADAPTIVE NEURO FUZZY INFERENCE SYSTEM.....................................................................22 5...........................................................................................................................51 .............................2 2........2..............43 8..............................................................................................................................................................3 Learning .................1 Division into frames and preprocessing ...................................

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