资料来源 : Free On-Line Dictionary of Computing

artificial neural network
     
         (ANN, commonly just "neural network"
        or "neural net") A network of many very simple processors
        ("units" or "neurons"), each possibly having a (small amount
        of) local memory.  The units are connected by unidirectional
        communication channels ("connections"), which carry numeric
        (as opposed to symbolic) data.  The units operate only on
        their local data and on the inputs they receive via the
        connections.
     
        A neural network is a processing device, either an
        {algorithm}, or actual hardware, whose design was inspired by
        the design and functioning of animal brains and components
        thereof.
     
        Most neural networks have some sort of "training" rule whereby
        the weights of connections are adjusted on the basis of
        presented patterns.  In other words, neural networks "learn"
        from examples, just like children learn to recognise dogs from
        examples of dogs, and exhibit some structural capability for
        generalisation.
     
        Neurons are often elementary non-linear signal processors (in
        the limit they are simple threshold discriminators).  Another
        feature of NNs which distinguishes them from other computing
        devices is a high degree of interconnection which allows a
        high degree of parallelism.  Further, there is no idle memory
        containing data and programs, but rather each neuron is
        pre-programmed and continuously active.
     
        The term "neural net" should logically, but in common usage
        never does, also include biological neural networks, whose
        elementary structures are far more complicated than the
        mathematical models used for ANNs.
     
        See {Aspirin}, {Hopfield network}, {McCulloch-Pitts neuron}.
     
        {Usenet} newsgroup: {news:comp.ai.neural-nets}.
     
        (1997-10-13)