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

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