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image classification using neural network
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%This code is written for image classification using Matlab newff function %You can refer Crab classification which is given in Matlab help %This is a supervised classification technique %Appropriate training areas are selcted for each class %Training should be given to the neural network using training areas %Here .CSV (comma seprated value) file is used to store training areas and %the corresponding class. Once the neural network is trained the entire %image can be converted to .CSV file for exmaple if the size of the RGB %image is 5 rows and 5 columns then the csv file will have 3 columns and 25 %rows. Each row will be one pixel of the image and each column will be one %band. First column will be red band 2nd will be green and 3rd will be blue band. %Once the CSV file for the entire image is ready it is given to the trained %neural network. 
engmso 2016-08-23
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Application of CMAC in system identification
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CMAC is a neural nework whose architecture is similar to that ofCerebellumtc "% CMAC is aneural nework whose architecture is similar to that of Cerebellum" % CMAC network's application in System Identification isimplemented in this program
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induction_motor
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asynchronous induction motor model in matlab simulink, fed from 3 phase source
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inverse kinematics
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inverse kinematics for 3 DOF robot manipulator from direct kinematics
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neural network
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matlab m file implementation for a neural network including training data
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fuzzy optmization using GD
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GD optimization of fuzzy controller for Vehicle control system simulation Based on page 186 in the text "Fuzzy Logic," by Yen and Langari. This m-file simulates a single discrete time step.
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Genetic Algorithm (GA)
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This program simulates the optimization of a simple function with a base-10 genetic algorithm. First, we outline some background information on GAs and define some terminology. Following this,
we explain how to use the program for your own purposes and as an example we show how to solve an optimization problem.
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