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

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