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

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