Practice - PRACTICE. NEURO-FUZZY LOGIC SYSTEMS MATLAB...

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PRACTICE. NEURO-FUZZY LOGIC SYSTEMS MATLAB TOOLBOX GUI Dmitry Bystrov, Jerker Westin
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2 Practice "Neuro-Fuzzy Logic Systems" are based on Heikki Koivo "Neuro Computing. Matlab Toolbox GUI" . Simulink for beginners section gives introduction to Matlab Toolbox, present users GUI for Matlab command window and Simulink. Fuzzy basics section describes the basic definitions of fuzzy set theory, i.e., the basic notions, the properties of fuzzy sets and operations on fuzzy sets. Are presented examples, exercises and laboratory works. Neural networks basics section gives introduction to neural networks. Are described the basic theoretical concepts. Backpropagation and radial basis function networks are reviewed with details. Are presented examples and laboratory works. ANFIS (Adaptive Neuro-Fuzzy Inference System) basic concepts are given in finally section. Are reviewed GENFIS1 and ANFIS commands, is presented exercise.
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3 Contents 1. Simulink for beginners. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Matlab command window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. Fuzzy basics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Fuzzy logic operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Fuzzy reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4. Laboratory work 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5. Sugeno- style fuzzy inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1. Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6. Laboratory work 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Neural networks basics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Radial Basis Function Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Backpropagation Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1. Backpropagation Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2. Assembling the data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3. Creating the network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4. Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.5. About training algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.6. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.7. Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Laboratory work 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6. Radial basis network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1. Neuron model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2. Network architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3. Design procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7. Laboratory work 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. ANFIS (Adaptive Neuro-Fuzzy Inference System). . . . . . . . . . . . . . . . . . . . . . 4.1. GENFIS1 and ANFIS Commands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Exercise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 6 8 8 11 12 13 24 24 28 34 38 39 40 46 49 62 64 65 65 65 66 67 68 68 69 69 70 71 72 73 74 75
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4 Chapter 1 Simulink for Beginners : To begin your SIMULINK session open first MATLAB ICON by clicking mouse twice and then type »simulink3 You will now see the Simulink block library Browse through block libraries. E.g., if you click Continuous, you will see the following: Observe the description of the integrator at the bottom and also the block that will be used for integrator, s 1 . This operator notation comes from linear systems domain, where s is Laplace variable. Roughly, s corresponds to derivative operator, dt d , and its inverse s 1 to integration, . Mathematics block library is shown below
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5 After browsing through other block libraries, we are now ready to start generating a simple Simulink diagram. Choose in menu selection File, then New and Model. This file will be called untitled .
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6 Untitled file, where the SIMULINK configuration is constructed using different blocks in the library. With a mouse you can configure your model into the empty space. 1.1. MATLAB Command window Once you have defined your system in SIMULINK window, you can simulate also on the MATLAB Command window. Save your model – currently it has the name Untitled , so use that.
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Practice - PRACTICE. NEURO-FUZZY LOGIC SYSTEMS MATLAB...

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