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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.......................................43 9..........2 Running the algorithm ..............................................................................................2 Operation neurons..........................................................................40 8......................................................................29 6.............................................. BACKPROPAGATION ALGORITHM ............. THE SPEECH SIGNAL ......................1........................................................................................................................................................................................................................................2 Backward pass ..................................29 6............................. NEURAL NETWORKS.............5 2..1 Network parameters ......37 8...............................................................................15 4...... THE REALIZATION THE PROGRAM..26 6..................................4........1....................2.....................1 The algorithm ..................................................................................................................................................1 The 'NNV' network.....................................................................................................................................4 Artificial neural networks ........................2 Analysis using filter banks ............32 8...5 2...................2 The ANFIS network...................9 3.............................................................................................28 6......................4 2.................................................................................................................................................................1 Network parameters .......1 Learning algorithm ANFIS .....TABLE CONTENTS LIST FIGURES LIST TABLES 1......................................... FUZZY SYSTEMS .........................................51 ........ CONCLUSION..............................43 8..........................................................17 5.1 Division into frames and preprocessing ..............................................1 Forward pass......................................1... ADAPTIVE NEURO FUZZY INFERENCE SYSTEM........................................................................1 Fuzzy Neural Networks ..........1.9 3...48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES ..............................1............................................................1.....6 3..........3 Learning ....................................2 2. INTRODUCTION ............................................................................3 Stop the training .........................40 8.............................................................22 5.................50 LIST INSERTS ..1 Description the backpropagation algorithm.........................................30 7.....12 3............................................................................................................................46 REFERENCES ....................................................40 8................................1 Signal preparation ...............................................................17 4..........14 3................................................................................................................................................................................2 Running the simulation ......................1 The basic Artificial Neuron........................................................................................1....................2........22 5.......2 Simulation results ...............................................24 5......................................2 2........................................................... THE SIMULATION ................................................1 2.............................................................43 8.......................................................................................1 Real brains.......

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