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 2.................................................................................................................. THE REALIZATION THE PROGRAM.........................................................................2...............................1 Division into frames and preprocessing .............................................2 Simulation results ...........29 6...............................................17 5.................50 LIST INSERTS ......43 8................................................................TABLE CONTENTS LIST FIGURES LIST TABLES 1..........................................................................................................................43 9.15 4..........................................29 6............. INTRODUCTION ..........................2 2........................................2 Running the algorithm ................................................................................................................26 6..4.......................1 The algorithm ....4 Artificial neural networks .............................. THE SPEECH SIGNAL ................................................................................... THE SIMULATION .............2 Analysis using filter banks ...................................2 Operation neurons.................1 Learning algorithm ANFIS ..............................................1.... BACKPROPAGATION ALGORITHM ...........................................1 Network parameters ..............1 Real brains....................................1..........................1 Fuzzy Neural Networks ................1...................48 LIST SYMBOLS, ABBREVIATIONS AND VARIABLES .............. NEURAL NETWORKS.......................................................................5 2.............43 8..................................9 3...........6 3...........................................32 8....................................................................................................12 3........................1............................................................................................... FUZZY SYSTEMS .............1 Network parameters ......................................30 7.............................................................2 2........................................1 The basic Artificial Neuron...........................................................................................................................................................................5 2.....................................3 Stop the training ................................................... CONCLUSION.......2..................................................14 3...............................................1............28 6.....2 Running the simulation .....9 3..................40 8...................................................................1...................... ADAPTIVE NEURO FUZZY INFERENCE SYSTEM.......................................................................4 2........................46 REFERENCES .....................................................51 .......................1 Description the backpropagation algorithm............22 5...............................................................................................................................40 8................................................................................................22 5.............................................................................................17 4...2 The ANFIS network..................1 The 'NNV' network....................................................................3 Learning .............................................24 5..2 Backward pass ....................................................................................................................1 Signal preparation .......1 Forward pass.................40 8............................................................1............37 8.................................................

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