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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........................................................................37 8................................2 2..............................................................1.........1.........................................1 Forward pass.....................................................................................................................46 REFERENCES ............................................................................2 Running the algorithm ...............................................................2 Backward pass ..................3 Learning ..........1... THE REALIZATION THE PROGRAM.................................................6 3.....2 Analysis using filter banks ............................43 8........................................................................................................1 The basic Artificial Neuron................................................................................32 8..................................................................2 Simulation results ...................................1 Division into frames and preprocessing .............................22 5...................................................................................................................................................................................................................................................................................................................... CONCLUSION.................................................29 6.............................1...................1 Fuzzy Neural Networks ................................. THE SPEECH SIGNAL ............51 .........................................14 3................3 Stop the training ....................26 6...................1.................40 8..............................................22 5.1 2....................................2 2........29 6................................................................................................................................................................................1 Learning algorithm ANFIS ..2...................2 Operation neurons................40 8.....................................................28 6................................................................................................1 Network parameters .........43 8......1 Description the backpropagation algorithm........2 The ANFIS network...................................4 Artificial neural networks ............15 4........................................................ ADAPTIVE NEURO FUZZY INFERENCE SYSTEM...........1 Real brains.............9 3...............................................................4.................................................24 5.......................TABLE CONTENTS LIST FIGURES LIST TABLES 1.......................1 Network parameters .......................................................................................................... INTRODUCTION ............................................................................. BACKPROPAGATION ALGORITHM .............................................1..............................................1 The algorithm ......................1..................................................................12 3....... NEURAL NETWORKS...................................43 9.........1 The 'NNV' network......2 Running the simulation ...........................................5 2........40 8............48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES ..........................................................................9 3.................................................................................... THE SIMULATION ...............2......50 LIST INSERTS .................................................................................................................1 Signal preparation ......17 4.17 5.......................30 7.5 2................. FUZZY SYSTEMS ................4 2.........................................................................................................

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