Klasifikace vzorů pomocí fuzzy neuronových sítí

| Kategorie: Diplomové, bakalářské práce  | Tento dokument chci!

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

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