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Artificial neural network

An artificial neural network and node technology, applied in the field of neural network models, can solve problems such as low calculation efficiency, large calculation amount, and weak learning effect, and achieve the effect of improving calculation efficiency, reducing calculation amount, and enhancing learning effect

Pending Publication Date: 2019-10-08
FU TAI HUA IND SHENZHEN +1
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AI Technical Summary

Problems solved by technology

[0004] However, the traditional artificial neural network requires a large amount of calculation, and the calculation efficiency is low
Moreover, after each node receives the input, it will provide output to the next layer of nodes. Due to the one-way signal transmission, the learning effect is not strong

Method used

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Embodiment Construction

[0020] see Figure 1 to Figure 4 , is a schematic structural diagram of the artificial neural network 1 in an embodiment of the present invention. The artificial neural network 1 includes an input layer 10 , an output layer 30 and a hidden layer 20 between the input layer 10 and the output layer 30 .

[0021] The input layer 10 includes a plurality of first nodes 11 . The hidden layer 20 includes a plurality of second nodes 21 . The output layer 30 includes a plurality of third nodes 31 .

[0022] The first node 11 is connected to the second node 21 via a first communication channel 100 . The first node 11 receives input data from a database (not shown in the figure), performs summation and nonlinear function transformation on the data, and outputs the transformed data to the second node 21 . The second nodes 21 are connected to each other through the second communication channel 200 , and are connected to the third node 31 through the third communication channel 300 . Th...

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Abstract

An artificial neural network comprises: an input layer comprising a plurality of first nodes; a hidden layer which comprises a plurality of second nodes; an output layer which comprises a plurality of third nodes, wherein the hidden layer is located between the input layer and the output layer, the hidden layer is of a three-dimensional structure, the plurality of planes are arranged at intervalsalong the direction from the input layer to the output layer, the second nodes are respectively positioned on the planes, the first nodes are connected with a plurality of second nodes located at oneplane closest to the input layer, the third nodes are connected with a plurality of second nodes positioned on one plane closest to the output layer, the second nodes located on each plane are connected with each other, and the second nodes located on every two adjacent planes are connected in the arrangement direction of the planes, so that at least one second node has six data transmission directions towards the other second nodes located on the same plane and the arrangement direction of the planes.

Description

technical field [0001] The invention relates to a neural network model, in particular to an artificial neural network. Background technique [0002] A neural network is a computing system that imitates the synapse-neuron structure of a biological brain for data processing. It consists of multi-layered computing nodes and connections between layers. Each node simulates a neuron and performs a specific operation, such as an activation function. The connection between nodes simulates a synapse, and the weighted value of the connection to the input from the upper layer node represents the synaptic weight. Neural networks have powerful nonlinear and adaptive information processing capabilities. [0003] The neuron in the artificial neural network processes the accumulated value from the connection input with the activation function as its own output. Corresponding to different network topologies, neuron models and learning rules, artificial neural networks include dozens of net...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/04G06N3/08
Inventor 林忠亿
Owner FU TAI HUA IND SHENZHEN
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