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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17 5......... CONCLUSION............................24 5...................1..........................1...................................................1 Fuzzy Neural Networks .........................32 8................................................................6 3..............2 Backward pass .......1 Network parameters .................................... BACKPROPAGATION ALGORITHM .....................26 6.1 Signal preparation ................................9 3.............................................2....1.......................... THE REALIZATION THE PROGRAM.........................................................................................................................TABLE CONTENTS LIST FIGURES LIST TABLES 1.........40 8...........................................................................................................................................40 8..............12 3.. THE SPEECH SIGNAL ..........................5 2........................................................51 .............9 3.......................29 6..... THE SIMULATION ........................................................ NEURAL NETWORKS.....................................................................1 The algorithm .......................................................17 4...3 Learning .............................................5 2.........37 8.........................................................1 2.......................................................1 Real brains......1.................1 Learning algorithm ANFIS ........................................30 7................................................................................3 Stop the training ...........................43 9............................................................................................................................................14 3......................................2 The ANFIS network...........1 The 'NNV' network............. ADAPTIVE NEURO FUZZY INFERENCE SYSTEM.................................................................................40 8.....................................................................................................................29 6....................................................................................2 Operation neurons..................................................................................................... INTRODUCTION .....15 4.......46 REFERENCES ..................................................................................................1 The basic Artificial Neuron........................................................................................................1 Forward pass..............................................22 5....................4 Artificial neural networks ..........2.................................................1.......................................................1 Network parameters ..........48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES ....................................................50 LIST INSERTS ...22 5......................................................2 2...............................................................................2 Simulation results ............................................28 6.........................................2 Analysis using filter banks .........2 Running the simulation ......................43 8................. FUZZY SYSTEMS .....................................2 Running the algorithm ......................................................................1......1 Division into frames and preprocessing .................43 8.....................................................................................................4...........................................................................................................................................................................1..................................................2 2.......................1 Description the backpropagation algorithm........................................4 2........

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