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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.....................................................................................................................................................................................................................................1 Network parameters ........................1 The algorithm .....................................................................................................2 Backward pass ..................4 2...............1 Learning algorithm ANFIS ............................................................1 2........................................................1 The basic Artificial Neuron.....1.........................................46 REFERENCES ..............43 9..............................................................................................................29 6..................................................................................1..26 6...........................TABLE CONTENTS LIST FIGURES LIST TABLES 1...2 The ANFIS network....2 Analysis using filter banks ...........................................2 Simulation results ..........12 3................................. ADAPTIVE NEURO FUZZY INFERENCE SYSTEM...........................................4.............. THE REALIZATION THE PROGRAM......................................40 8..................1................................ CONCLUSION...............................24 5..........15 4..1......................1 Forward pass...9 3.....................................................22 5....................................................................................................................................... BACKPROPAGATION ALGORITHM ..........................6 3......14 3.............................. THE SPEECH SIGNAL ..........5 2...........1 The 'NNV' network..............................................................................................30 7....................... FUZZY SYSTEMS ..........................................................43 8..1...............................................................................................................................2 2...................................................................................1 Description the backpropagation algorithm...........................................................................................32 8.22 5.......................................................................................................2 2...............................................................................29 6..........2.17 4.................................................3 Learning ...............................................37 8.1.........2.................. NEURAL NETWORKS.......1 Division into frames and preprocessing ...........................................................................1 Fuzzy Neural Networks .............................. THE SIMULATION ................... INTRODUCTION ..............................................................................40 8.............1 Signal preparation .............................................................................................................................................................................................2 Running the simulation ..................5 2................................17 5..........................1 Network parameters ........................................................51 ..................................28 6..............................................4 Artificial neural networks .........................................................2 Operation neurons.................................43 8....................................................................................................48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES .................................2 Running the algorithm .........1 Real brains..............40 8....................................9 3..........................50 LIST INSERTS .....................3 Stop the training ................1...........................................

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