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 .......................................................28 6................43 8..................... FUZZY SYSTEMS ............4 2.........................................................2 Running the algorithm .29 6...................1 The algorithm .......................5 2.....................................................48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES .......................................................................29 6...............12 3................................................................... INTRODUCTION ..............................................................9 3......................................1........................................ ADAPTIVE NEURO FUZZY INFERENCE SYSTEM.....................................................................................................................40 8.......................................................................... THE SPEECH SIGNAL ......................................................................37 8.................................................2 2..........................6 3..........................1 Signal preparation ............................14 3.............................................15 4.......... BACKPROPAGATION ALGORITHM ................................1 The 'NNV' network.............................1 2.............................1....2 Backward pass ......40 8.............................................40 8........................1 Fuzzy Neural Networks ...................................... CONCLUSION.............................................22 5.............51 ....1...............1 The basic Artificial Neuron.........................................................................................................................24 5.............................................1.................................46 REFERENCES ....43 8.............................1 Division into frames and preprocessing ...............................................................2 The ANFIS network....................................................................................................1 Description the backpropagation algorithm.............9 3.............................................................2 Operation neurons.................4 Artificial neural networks ..................43 9.......................................................................................................2 Simulation results ........................................................................................1............................................................22 5.......................................................................................................................1.................32 8..................2.........................................................5 2.....................................................................................................................2 Running the simulation .........................................................2 2.........1 Learning algorithm ANFIS ...............................................................................2 Analysis using filter banks ................................................................................................4.................................................................................................17 5.17 4.............1 Network parameters .................................................2....1 Real brains.....................3 Stop the training .................26 6...........50 LIST INSERTS ...... THE REALIZATION THE PROGRAM......1 Forward pass..............30 7..............................3 Learning ............TABLE CONTENTS LIST FIGURES LIST TABLES 1........ THE SIMULATION .............. NEURAL NETWORKS.................................................................................................................................................................1...............................................................................

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