A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes.[1] Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be ?1 and 1.
神经网络是神经元的网络或回路，在现代，人工神经网络是由一系列人工神经元或节点构成的。因此一个神经网络可以是一个生物神经网络，由一系列真实的生物神经元组成，或者一个解决人工智能问题的人工神经网络。生物神经元之间的连接被模型化为权重。正权重反应一个对连接的刺激，负值表示对连接的阻碍。所有输入被权重修改并加和。这个行为适用于线性结合。最终，一个激活函数控制输出的幅度。比如，输出的可接受范围经常是在0和1之间，或者-1和1。
These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information.[2]
这些人工神经网络可以被用在预测模型，适应度控制和通过数据集可训练的应用上。学习经验的自我学习可在网络中体现，能从一些复杂但是看上去无关的信息集合得出结果。
原文：https://www.cnblogs.com/yuelien/p/13732342.html