Neural networks in matlab
[DOC File]THE HONG KONG POLYTECHNIC UNIVERSITY
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EIE520 Neural Computation. Lab 2: 2-D Vowel Recognition using Kernel-Based Neural Networks. A. Introduction: There are many methods to perform pattern classification using neural networks. Among these methods, Gaussian Mixture Models (GMMs) and Radial Basis Function (RBF) networks are two of the most promising neural models for.
[DOC File]Using Genetic Algorithms as a Controller for Hot Metal ...
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The approach of neural networks provides a more straightforward way to handle problems involving several inter-dependent variables. Neural networks are inherently parallel machines that have the ability of modeling complex mathematical functions between inputs and outputs even when the form of the function is unknown.
[DOC File]EE 556 Neural Networks - Course Project
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EE 556 Neural Networks - Course Project. Technical Report. Isaac Gerg and Tim Gilmour. ... We developed Matlab code based on the equations in the papers, and tested the algorithms on a cocktail-party audio simulation. Our original plan was to compare the FastICA algorithm with a specialized “Two-Source ICA” algorithm presented in [3], but ...
[DOC File]Making Prediction Intervals Using Neural Networks
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For Neural Networks. Claus Benjaminsen. ECE 539 12/21 2005 Abstract. This project describes a way of estimating prediction intervals using clustering. The method is developed and implemented in matlab, and tested on a small 2D dataset. The performance of the method is compared with the standard method of making prediction intervals using ...
[DOC File]LECTURE #9: FUZZY LOGIC & NEURAL NETS
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Fundamentals of Neural Networks: Architecture, Algorithms, and Application, Prentice Hall (1994). Simon Haykin. Nicknamed the bible of neural networks by my students this 700-page work can be considered both as a desktop reference and advanced graduate level text on neural networks with challenging homework problems.
[DOC File]MIT - Massachusetts Institute of Technology
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A neural network is made up of one or more neurons, organized into one or more layers. The layer that receives the network input is called the hidden layer and the layer that outputs the network output is called the output layer. Neural networks can have one or more layers between the input and output layers. These layers are called hidden layers.
[DOC File]Neural Networks - DMU
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In matlab we usually call the hardlimit function hardlim. In general we can have more than one neural unit in a layer. A calculation with a two input perceptron with a single neuron. Inputs are vectors with two values . x [2 by 1 vector]. The weight matrix is . w [1 by 2] matrix. There is a [ 1 by 1] bias . b (don't worry about this for now).
[DOC File]Optimizing Decision Making with Neural Networks in ...
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Neural networks can learn to make human-like decisions, and would naturally follow any changes in the data set as the environment changes, eliminating the task of re-tuning the coefficients. ... For implementation we used a Matlab 6.1 [30] environment with at least a 1GHz Pentium IV processor. For data acquisition and preprocessing we used SQL ...
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Artificial Neural Network is a tool that can find the relation between input parameters and output parameter without generating correlation. By applying the Artificial Neural Networks, directional driller are able to analyze build rate prediction based on steering behavior of drilling BHA.
[DOC File]NEURAL NETWORKS FOR FAULT DIAGNOSIS BASED ON MODEL ERRORS ...
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• A special strength of neural nets. Neural Net for classification at run time. Neural Networks vs. other techniques • Complements traditional modelling, rule-based systems, optimization, regression, interpolation, and control • Focus is on nonlinear systems, vs. traditional linear techniques that may be more efficient for linear systems
[DOC File]IMAGE COMPRESSION AND DECOMPRESSION USING
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The project “IMAGE COMPRESSION AND DECOMPRESSION USING NEURAL NETWORKS” has been successfully programmed using MATLAB and tested. The computing world has a lot to gain from neural networks. Their ability to learn by example makes them very flexible and powerful.
[DOC File]Basic functions implemented in our neural networks algorithm:
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Basic functions implemented in the neural networks algorithm: A one hidden layer MLP network with feed-forward Trained with back-propagation Different and randomly selected training and test data sets. Software used: MATLAB (Run on Windows Platform) Important Considerations:
[DOC File]Analysis of Trained Neural Networks
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Neural Networks are typically thought of as black boxes trained to a specific task on a large number of data samples. In many applications it becomes necessary to “look inside” of these black boxes before they can be used in practice. This is done in case of high risk applications or applications with a limited number of training samples.
[DOC File]Neural Network Prediction - CAE Users
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Then the coding began in Matlab. I wrote 2 M files: trainandtest.m and predict.m. These files utilized the neural network toolbox and some of the coding techniques from the course. The only outside code included was Prof. Hu’s randomizing function. Both programs are interactive and rather flexible.
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