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。
Artificial neural networks (ANNs), usually simply called neural networks(NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.[1]
人工神经网络，常被简称为神经网络，是计算系统被构造生物大脑神经网络所启发来的。
An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
人工神经网络基于一系列称作人工神经元的单元或节点，这些（人工神经元）在生物的大脑中构建神经。每个连接就像生物脑中的突触，将信号传递给其他的神经元。一个人造神经元接收信号，对其进行加工并且可连接其他信号神经元。每个连接的信号是一个实数，计算其输入加和的非线性函数计算每个神经元的输出。连接被称为边。神经元和边一般有调整学习进程的权重。权重增加或减少连接中信号的强度。神经元可能会有阈值，这样一来，只有在信号强度之和可以通过阈值时才发送信号。一般地，神经元构成层，不同的层会表达对于输出的不同转换。信号从第一层传向最后一层，有可能重复多次。