神经网络学习文章

9.3 Neural Networks

Neural networks are an interesting topic for artificial intelligence researchers. Since they are related to man’s nervous system construction, the results of their investigations form a foundation on which hypotheses describing the working of a brain can be built [1].

Neural Network Objectives

Although various neural network models to a greater or a lesser extent remind one of the actual neural network, nevertheless they do differ from it (apart from cases where such models are constructed with the intention of an exact simulation). The purpose of the limits imposed is to simplify the reasoning, and to enable the executability of algorithms devised [2].

Artificial neural networks enable the execution of certain intelligent operations, e.g. associations.

The analysis of these networks also provides interesting conclusions on parallel processing. Neural cells are characterized by a very long response (reaction) time (i.e. they work slowly), but because of [3] the massively parallel processing man is capable of instant execution of tasks for which conventional sequential machines require significantly more time; for instance, image recognition. Investigations of such processes will eventually lead to the design of fast algorithms that use the parallelism of artificial neurons. Parallel processing is especially important in building search algorithms employing neural networks.

In the last couple of years electronic cubes containing more and more densely packed neural networks have appeared.

Neural Network Structure

A neural network is a set of processing units (nodes) joined by links through which they communicate. Each unit is characterized by its activation state which changes in time. From the current unit's activation state a signal it sends into the network is calculated [4]. This signal is carried over the links to other nodes. During the transmission it may be weakened or strengthened, depending upon the link's characteristics. Signals reaching a unit from its neighbors are combined into an input signal, from which the next activation state of that unit is computed [5].

Network definition comprises descriptions of the following elements:

⏹set of nodes

⏹links

⏹rules for calculating the input signal

⏹activation function

⏹output signal function

We should start the characterization of processing units (neurons) by first indicating the significance of each unit. In some of the systems, single nodes clearly represent defined objects, features or concepts. This representation method is called local. Its opposite is a distributed representation, where single units denote not whole concepts but rather their abstract parts which do not have a representation in the language in which the working of a network is interpreted [6]. Only subsets of the whole network form full concept descriptions.

A processing unit executes a simple sequence of operations: receives signals from neighbors, computes its activation state, and sends that state into the network. Not all units have to have the same characteristics (activation function, input signal), although networks