Difference in Learning ability of Neural Nets and Logical Expressions

为了找出人工智能中神经网络与逻辑表达式在学习能力上的差异，首先分析了神经网络对小于脉冲宽度的时间差的检测能力的来源。提出了实现上述检测的并行处理机制，并通过计算机仿真进行了验证。神经网络必须具有自学习能力，以产生各种不同的序列，并能够从序列中识别适当的序列。这些能力的机制进行了分析，并提出了一个神经网络模型。在实际环境中对该模型进行了计算机仿真，仿真结果表明该模型能够有效地生成序列和选择特定序列.针对不需要执行网的可达集的问题，提出了一类Petri网，该网是逻辑类型的典型表示.换句话说，可达集是由网络的结构和初始状态来确定的，这两种理解的结合使我们清楚地看到，时间可微性是神经网络优于逻辑表达式的最关键的。

The purpose of this study is to identify the difference between the neural nets and the logical expressions in the learning ability for the artificial intelligence.The origin of capability of detecting the time difference smaller than the pulse duration used in the neural nets is first analyzed. The parallel processing mechanism which enables the above detection is proposed and verified by the computer simulation. It is necessary for the neural nets to have selflearning capability to generate wide variety of different sequences and capability of identifying the adequate sequence from the sequences. The mechanism for these capability is analyzed and proposed as a neural net model. The computer simulation of this model in the realistic environment shows that the proposed model is sufficient for generating the sequences and selecting a particular sequence.A class of Petri nets, which is a typical expression of the logical type, is proposed for the reachability set of the net is to be identified without carrying out the execution of the nets. In other words, the reachability set is identified for the class of the nets from the structure of the nets and the initial states.Combination of these two understandings makes us clear that the time differentiability is the most crucial to give advantage of the neural nets over the logical expressions.