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

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