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| − | ==== 人工神经网络 Artificial neural networks ==== | + | ==== 人工神经网络==== |
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| | [[File:Colored neural network.svg|thumb|300px|An artificial neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in a [[brain]]. Here, each circular node represents an [[artificial neuron]] and an arrow represents a connection from the output of one artificial neuron to the input of another.'''人工神经网络 Artificial Neural Network，ANN'''是一组相互连接的节点，类似于大脑中庞大的神经元网络。在这里，每个圆形节点代表一个人工'''神经元 Neuron'''，一个箭头代表从一个人工神经元的输出到另一个输入的连接]] | | [[File:Colored neural network.svg|thumb|300px|An artificial neural network is an interconnected group of nodes, akin to the vast network of [[neuron]]s in a [[brain]]. Here, each circular node represents an [[artificial neuron]] and an arrow represents a connection from the output of one artificial neuron to the input of another.'''人工神经网络 Artificial Neural Network，ANN'''是一组相互连接的节点，类似于大脑中庞大的神经元网络。在这里，每个圆形节点代表一个人工'''神经元 Neuron'''，一个箭头代表从一个人工神经元的输出到另一个输入的连接]] |
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| − | :''主文章：[[ 人工神经网络]]''
| + | [[人工神经网络]](ANN)学习算法，通常称为神经网络(NN)，是一种受生物神经网络启发的学习算法。计算是根据一组相互关联的[https://en.wikipedia.org/wiki/Artificial_neuron 人工神经元]来构造的，它们使用连接主义的计算方法来处理信息。现代神经网络是非线性统计数据建模工具。它们通常用于模拟输入和输出之间的复杂关系，在数据中找到模式，或在观测变量之间的未知[[联合概率分布]]中寻找统计结构。 |
| − | [[人工神经网络]](ANN)学习算法，通常称为神经网络(NN)，是一种受[https://en.wikipedia.org/wiki/Neural_circuit 生物神经网络]启发的学习算法。计算是根据一组相互关联的[https://en.wikipedia.org/wiki/Artificial_neuron 人工神经元]来构造的，它们使用[https://en.wikipedia.org/wiki/Connectionism 连接主义]的[https://en.wikipedia.org/wiki/Computation 计算]方法来处理信息。现代神经网络是[https://en.wikipedia.org/wiki/Non-linear 非线性][https://en.wikipedia.org/wiki/Statistical 统计][https://en.wikipedia.org/wiki/Data_modeling 数据建模]工具。它们通常用于模拟输入和输出之间的复杂关系，在数据中[https://en.wikipedia.org/wiki/Pattern_recognition 找到模式]，或在观测变量之间的未知[https://en.wikipedia.org/wiki/Joint_probability_distribution 联合概率分布]中寻找统计结构。 | |
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| − | An ANN is a model based on a collection of connected units or nodes called "[[artificial neuron]]s", which loosely model the [[neuron]]s in a biological [[brain]]. Each connection, like the [[synapse]]s in a biological [[brain]], can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a [[real number]], and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a [[weight (mathematics)|weight]] that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of 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.
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| − | An ANN is a model 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 information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial 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. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of 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.
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| | 人工神经网络是一种基于一组被称为“人工神经元”的连接单元或节点的模型，人工神经元可以对生物大脑中的神经元进行松散的建模。每一个连接，就像生物大脑中的突触一样，可以将信息，一个“信号” ，从一个人工神经元传递到另一个。接收到信号的人工神经元可以处理它，然后发送信号给连接到它的其他人工神经元。在通常的人工神经网络实现中，人工神经元之间连接处的信号是一个实数，每个人工神经元的输出是由一些输入和的非线性函数计算出来的。人造神经元之间的连接称为“边缘”。人工神经元和边缘通常有一个权重，可以随着学习的进行而调整。重量增加或减少连接处信号的强度。人工神经元可能有一个阈值，这样只有当聚合信号超过这个阈值时才发送信号。通常，人造神经元聚集成层。不同的层可以对其输入执行不同类型的转换。信号从第一层(输入层)传输到最后一层(输出层) ，可能是在多次遍历这些层之后。 | | 人工神经网络是一种基于一组被称为“人工神经元”的连接单元或节点的模型，人工神经元可以对生物大脑中的神经元进行松散的建模。每一个连接，就像生物大脑中的突触一样，可以将信息，一个“信号” ，从一个人工神经元传递到另一个。接收到信号的人工神经元可以处理它，然后发送信号给连接到它的其他人工神经元。在通常的人工神经网络实现中，人工神经元之间连接处的信号是一个实数，每个人工神经元的输出是由一些输入和的非线性函数计算出来的。人造神经元之间的连接称为“边缘”。人工神经元和边缘通常有一个权重，可以随着学习的进行而调整。重量增加或减少连接处信号的强度。人工神经元可能有一个阈值，这样只有当聚合信号超过这个阈值时才发送信号。通常，人造神经元聚集成层。不同的层可以对其输入执行不同类型的转换。信号从第一层(输入层)传输到最后一层(输出层) ，可能是在多次遍历这些层之后。 |
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| − | The original goal of the ANN approach was to solve problems in the same way that a [[human brain]] would. However, over time, attention moved to performing specific tasks, leading to deviations from [[biology]]. Artificial neural networks have been used on a variety of tasks, including [[computer vision]], [[speech recognition]], [[machine translation]], [[social network]] filtering, [[general game playing|playing board and video games]] and [[medical diagnosis]].
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| − | The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.
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| | 人工神经网络方法的最初目标是用人类大脑解决问题的同样方式。然而，随着时间的推移，其注意力转移到执行特定的任务上，导致了与生物学的偏差。现在人工神经网络已被用于各种任务中，包括'''计算机视觉 Computer Visio'''、'''语音识别 Speech Recognition'''、'''机器翻译 Machine Translation'''、'''社会网络过滤 Social Network Filtering'''、玩棋盘和视频游戏以及医疗诊断。 | | 人工神经网络方法的最初目标是用人类大脑解决问题的同样方式。然而，随着时间的推移，其注意力转移到执行特定的任务上，导致了与生物学的偏差。现在人工神经网络已被用于各种任务中，包括'''计算机视觉 Computer Visio'''、'''语音识别 Speech Recognition'''、'''机器翻译 Machine Translation'''、'''社会网络过滤 Social Network Filtering'''、玩棋盘和视频游戏以及医疗诊断。 |