Chapter 5: Introduction to Neural Networks and the Backpropagation Algorithm


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This chapter introduces feed-forward , multi-layer neural network architectures with learning provided by the backpropagation algorithm. Backpropagation is likely the most important learning algorithm for neural networks and has contributed to a resurgence of biologically-inspired methods for computation. After detailing neural networks and backpropagation, we'll look at the resulting network's application in game character AI.

While a large variety of neural network topologies and learning algorithms exist, this chapter will focus on feed-forward, multi-layer networks using backpropagation learning. We'll begin with a simple introduction of neural networks and their components ; discuss the learning algorithm and then some problems that can arise during backpropagation learning. We'll look at an example of a simple network and walk through the backpropagation algorithm to understand its properties. Finally, we'll look at simple neural networks as a way to give life to characters within game environments.

Neural Networks From the Biological Perspective

Neural networks are very simple implementations of local behavior observed within our own brains . The brain is composed of neurons, which are the individual processing elements. Neurons are connected by axons that end at the neuron in a synapse. The synapse is responsible for relaying a signal to the neuron . Synapses can be either inhibitory or excitory.

The human brain contains approximately 10 11 neurons. Each neuron connects to approximately 1000 other neurons, except in the cerebral cortex where the density of interneuron connectivity is much higher. The structure of the brain is highly cyclic (self-referential), but it can be thought of as having a layered architecture (see Figure 5.1). In a very simplified model, the input layer to the network provides our sensory inputs from the environment, the middle layer, or cerebral cortex , processes the inputs, and the output layer provides motor control back to the environment.

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Figure 5.1: Layered architecture for a simple brain.

Artificial neural networks attempt to mimic the basic operation of the brain. Information is passed between the neurons, and based upon the structure and synapse weights, a network behavior (or output mapping) is provided.




Visual Basic Developer
Visual Basic Developers Guide to ASP and IIS: Build Powerful Server-Side Web Applications with Visual Basic. (Visual Basic Developers Guides)
ISBN: 0782125573
EAN: 2147483647
Year: 1999
Pages: 175

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