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<AI : Neural Network for beginners (Part 2 of 3) ⁄ >

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This article is part 2 of a series of 3 articles that I am going to post. The proposed article content will be as follows:

1. Part 1 : This one, will be an introduction into Perceptron networks (single layer neural networks)
2. Part 2 : Will be about multi layer neural networks, and the back propogation training method to solve a non linear classification problem such as the logic of an XOR logic gate. This is something that a Perceptron can't do. This is explained further within this article
3. Part 3 : Will be about how to use a genetic algorithm (GA) to train a multi layer neural network to solve some logic problem


This article will show how to use a multi-layer neural network to solve the XOR logic problem.
A Brief Recap ( From part 1 of 3)

Before we commence with the nitty gritty of this new article which deals wtih multi layer Neural Networks, let just revisit a few key concepts. If you haven't read article1, perhaps you should start there.

Perceptron Configuration ( Single layer network)

The inputs (x1,x2,x3..xm) and connection weights (w1,w2,w3..wm) shown below are typically real values, both positive (+) and negative (-).

The perceptron itself, consists of weights, the summation processor, and an activation function, and an adjustable threshold processor (called bias here after).

For convenience the normal practice is to treat the bias, as just another input. The following diagram illustrates the revised configuration.

este é só um excerto do artigo, para aceder ao artigo completo, clique no link em baixo:
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