Thesis: "Face recognition using neural network : final report" by ...
Jul 22, 2008 ... This document presents how a face recognition system can be designed with artificial neural network.
APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN THE ...
Alternatively we discuss methods of using the topology and constraints of the problems themselves to design the topology and connections of the neural solution. We define several useful circuits-generalizations of the Winner-Take-All circuitthat allows us to incorporate constraints using feedback in a controlled manner. These circuits are proven to be stable, and to only converge on valid states. We use the Hopfield electronic model since this is close to an actual implementation. We also discuss methods for incorporating these circuits into larger systems, neural and nonneural. By exploiting regularities in our definition, we can construct efficient networks. To demonstrate the methods, we look to three problems from communications. We first discuss two applications to problems from circuit switching; finding routes in large multistage switches, and the call rearrangement problem. These show both, how we can use many neurons to build massively parallel machines, and how the Winner-Take-All circuits can simplify our designs.
Next we develop a solution to the contention arbitration problem of high-speed packet switches. We define a useful class of switching networks and then design a neural network to solve the contention arbitration problem for this class. Various aspects of the neural network/switch system are analyzed to measure the queueing performance of this method. Using the basic design, a feasible architecture for a large (1024-input) ATM packet switch is presented. Using the massive parallelism of neural networks, we can consider algorithms that were previously computationally unattainable. These now viable algorithms lead us to new perspectives on switch design.
Hand Gesture Recognition Using Neural Networks Thesis ...
A good way to introduce the topic is to take a look at a typical application of neural networks. Many of today's document scanners for the PC come with software that performs a task known as optical character recognition (OCR). OCR software allows you to scan in a printed document and then convert the scanned image into to an electronic text format such as a Word document, enabling you to manipulate the text. In order to perform this conversion the software must analyze each group of pixels (0's and 1's) that form a letter and produce a value that corresponds to that letter. Some of the OCR software on the market use a neural network as the classification engine.
The demonstration of a neural network learning to model using the exclusive-or (Xor) data. The Xor data is repeatedly presented to the neural network. With each presentation, the error between the network output and the desired output is computed and fed back to the neural network. The neural network uses this error to adjust its weights such that the error will be decreased. This sequence of events is usually repeated until an acceptable error has been reached or until the network no longer appears to be learning.
Master Thesis: A modular neural network architecture ...
The diagram above is an two hidden layer Multiplayer Perceptron (MLP). The inputs are fed into the input layer and get multiplied by interconnection weights as they are passed from the input layer to the first hidden layer. Within the first hidden layer, they get summed then processed by a nonlinear function (usually the hyperbolic tangent). As the processed data leaves the first hidden layer, again it gets multiplied by interconnection weights, then summed and processed by the second hidden layer. Finally the data is multiplied by interconnection weights then processed one last time within the output layer to produce the neural network output.
The most common neural network model is the Multilayer Perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that correctly maps the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown. A graphical representation of an MLP is shown below:
Stock Price Prediction using Neural Networks Master Thesis Leiden ...
(2012) Evolution of neural networks. EngD thesis.
File Format: PDF/Adobe Acrobat In this thesis, it will be shown that Artificial Neural Networks (ANN) are capable ...
Research Paper on Basic of Artificial Neural Network – …
This thesis evaluates a possible use of artificial neural networks for military manpower and personnel analysis.
Phd Thesis Artificial Neural Network
File Format: PDF/Adobe Acrobat Although neural networks have been applied to medical problems in recent years ...
a suite of master thesis neural networks as ..
Of course character recognition is not the only problem that neural networks can solve. Neural networks have been successfully applied to broad spectrum of data-intensive applications, such as:
Learning algorithms for neural networks - CaltechTHESIS
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Introduction to Artificial Neural Networks (PDF …
The demonstration of a neural network used within an optical character recognition (OCR) application. The original document is scanned into the computer and saved as an image. The OCR software breaks the image into sub-images, each containing a single character. The sub-images are then translated from an image format into a binary format, where each 0 and 1 represents an individual pixel of the sub-image. The binary data is then fed into a neural network that has been trained to make the association between the character image data and a numeric value that corresponds to the character. The output from the neural network is then translated into ASCII text and saved as a file.
Birge phd thesis neural networks: ..
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Phd Thesis On Neural Networks - Sandra Coleman …
This thesis deals mainly with the development of new learning algorithms and the study of the dynamics of neural networks. We develop a method for training feedback neural networks. Appropriate stability conditions are derived, and learning is performed by the gradient descent technique. We develop a new associative memory model using Hopfield's continuous feedback network. We demonstrate some of the storage limitations of the Hopfield network, and develop alternative architectures and an algorithm for designing the associative memory. We propose a new unsupervised learning method for neural networks. The method is based on applying repeatedly the gradient ascent technique on a defined criterion function. We study some of the dynamical aspects of Hopfield networks. New stability results are derived. Oscillations and synchronizations in several architectures are studied, and related to recent findings in biology. The problem of recording the outputs of real neural networks is considered. A new method for the detection and the recognition of the recorded neural signals is proposed.
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