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Perceptron: Perceptron implementation for pattern recognition

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  Author Author  
Picture of André Paterlini Oliveira Vieira
Name: André Paterlini Oliveira ... <contact>
Classes: 1 package by
Country: Brazil Brazil
Age: 32
All time rank: 47041 in Brazil Brazil
Week rank: 783 Down67 in Brazil Brazil Down
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  Detailed description   Download Download .zip .tar.gz  
This class implements a model of the Percetron (Artificial Neural Networks - ANN) that can be trained to recognize patterns in its inputs.

This is similar to the algorithm used on palmtops to recognize words written on its pen pad.

The example that comes with this class demonstrates how it can be used to find people that match the profile an inquiring user that fills a form with questions about personal preferences.

First the perceptron is trained with answers of several people. Then the answers of an inquiring user are converted into values that are fed to the perceptron in order to find profile matches.

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Name: Perceptron
Base name: perceptron
Description: Perceptron implementation for pattern recognition
Version: -
PHP version: -
License: Free For Educational Use
All time users: 5633 users
All time rank: 388
Week users: 2 users
Week rank: 882 Down
 
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Group folder image Algorithms Numerical and statistical algorithms View top rated classes
Group folder image Artificial intelligence Automation of tasks using human-like intelligence View top rated classes


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PHP Programming Innovation award winner
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Neural Networking is a computing science field that is not so well known among most software developers, but it can help solving problems that are difficult to solve with conventional computing methods.

Neural Networking consists of emulating the way a brain works by the means of networks of interconnected neurons. These neurons have inputs and outputs. They receive input signals that are combined according to some internal factors to define their output signals.

The neural networks need to be trained to perform some useful tasks. Usually these tasks consist of perceiving patterns in the input signals of their neurons. This is why they are also known as perceptrons.

Training consist of submitting a reasonably large sample of subjects that need to be recognized. Iteratively the values of internal factors of each neuron are adjusted until they output the desired response with an acceptable error rate.

The most common applications of neural networks are usually recognizing patterns that otherwise would be hard to distinguish by common computing methods, like for instance, recognizing handwritten characters in a scanned image.

This class provides an implementation of a perceptron. It comes with an example application of classifying users by type of profile based on the answers that they provide to questions presented in a Web form.

Since the class provides a generic implementation of a perceptron, there could be many other examples of application for this class, possibly only limited by the imagination of the developers that understand its potential.

Manuel Lemos

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  Files folder image Files  
File Role Description
Accessible without login Plain text file como_funciona Doc. F.A.Q - em português
Accessible without login Plain text file find_match.php Example Example - Finds a class pattern from the submited user profile (see index.html - HTML form)
Accessible without login Plain text file how_does_it_works Doc. F.A.Q.
Accessible without login HTML file index.html Data Example - RNA Perceptron implementation form
Plain text file perceptron_class.php Class Class - RNA Perceptron OOP class

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