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...............................3 Learning ......29 6..46 REFERENCES ...................................................1 Signal preparation .....43 8...............................................................................12 3.....2 2...............................22 5................................................................................................................................1.......... ADAPTIVE NEURO FUZZY INFERENCE SYSTEM.......................................1 2................................1 Division into frames and preprocessing ............................................................................1 The algorithm ......1 Network parameters ..................................................................................................1 Real brains............9 3..................................................................................43 9....................................5 2...................................................................................................................................................17 5.....................................................1.....................17 4..1 Fuzzy Neural Networks .........1...................................37 8............................................................... THE SIMULATION .....29 6......................................40 8............................................................... THE SPEECH SIGNAL ..........................................4 2..................................................9 3...................................................................................3 Stop the training ..............50 LIST INSERTS ......1 The 'NNV' network.15 4........................ BACKPROPAGATION ALGORITHM ...........1 Network parameters .......5 2...............................................................................................................................................................48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES .....................................................................1...............4 Artificial neural networks .............................30 7.........1.................................2..................................1 Forward pass........................1 Learning algorithm ANFIS ..............40 8..........14 3.....................................................................2 2................................................24 5.26 6............................................................................................2 Analysis using filter banks ....................................................................22 5................................. THE REALIZATION THE PROGRAM.......................................................................................................................................... INTRODUCTION ..........................................28 6........................................................................................43 8................. CONCLUSION...2 Backward pass ................................TABLE CONTENTS LIST FIGURES LIST TABLES 1.........................................................2 The ANFIS network.........................................2 Running the algorithm ............................................................40 8..............................................................1..................................2................................................................... NEURAL NETWORKS............................2 Operation neurons..............2 Simulation results ......... FUZZY SYSTEMS ....................32 8.1 The basic Artificial Neuron.............................................4............................................................51 ................................................................................1 Description the backpropagation algorithm..............................................................................................................................2 Running the simulation .............6 3.

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