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==图片==

==图片==

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File:Single layer ann.svg|A single-layer feedforward artificial neural network. Arrows originating from <math>\scriptstyle x_2</math> are omitted for clarity. There are p inputs to this network and q outputs. In this system, the value of the qth output, <math>\scriptstyle y_q</math> would be calculated as <math>\scriptstyle y_q = K*(\sum(x_i*w_{iq})-b_q) </math>

 

File:Two layer ann.svg|A two-layer feedforward artificial neural network.

File:Single_layer_ann.svg.png|一个单层前馈人工神经网络。从<math>\scriptstyle x_2</math>开始的箭头为了清晰省略了。这个网络有p个输入和q个输出。在这个系统中，第q个输出的值<math>\scriptstyle y_q</math>被以<math>\scriptstyle {y_q} = K*({\sum({x_i}*{w_{iq}})}-{b_q}) </math>计算

File:Artificial neural network.svg|An artificial neural network.

File:Artificial_neural_network.svg.png|一个人工神经网络

File:Ann dependency (graph).svg|An ANN dependency graph.

File:Ann_dependency_(graph).svg.png|一个ANN依赖图

File:Single-layer feedforward artificial neural network.png|A single-layer feedforward artificial neural network with 4 inputs, 6 hidden and 2 outputs. Given position state and direction outputs wheel based control values.

File:Single-layer_feedforward_artificial_neural_network.png|有4输入，6隐藏单元和2输出的单层前馈神经网络。给定位置状态和方向，输出转动基于控制值。

File:Two-layer feedforward artificial neural network.png|A two-layer feedforward artificial neural network with 8 inputs, 2x8 hidden and 2 outputs. Given position state, direction and other environment values outputs thruster based control values.

File:Two-layer_feedforward_artificial_neural_network.png|有8输入，2x8隐藏单元和2输出的两层前馈人工神经网络。给定位置状态，方向和其他环境值，输出推进基于控制值。

File:cmac.jpg|Parallel pipeline structure of CMAC neural network. This learning algorithm can converge in one step. 

File:Cmac.jpg|CMAC神经网络的并行流水线结构。这种学习算法可以一步收敛。

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