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Method and device for constructing MADALINE neural network based on sensitivity

A neural network and sensitivity technology, applied in the field of network construction, can solve the problem that the number of neurons in the hidden layer cannot be guaranteed, and achieve the effect of improving classification performance and good generalization

Inactive Publication Date: 2015-04-08
HOHAI UNIV
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Problems solved by technology

Even so, there is no guarantee that the number of neurons in the hidden layer is optimal

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  • Method and device for constructing MADALINE neural network based on sensitivity
  • Method and device for constructing MADALINE neural network based on sensitivity

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Embodiment

[0028] Now take the MADALINE neural network as an example to illustrate the method for selecting samples of the forward neural network according to the present invention.

[0029] The MADALINE neural network is a fully connected feed-forward neural network suitable for object classification. The structure of the MADALINE neural network is as follows figure 1 As shown, it is a three-layer feed-forward network: the input layer MA is composed of input pattern nodes, x i Represents the i-th component of the input pattern vector (i=1,2,...,n); the second layer is the hidden layer MB, which consists of m nodes b j (j=1,2,...,m) composition. The third layer is the output layer MC, which consists of p nodes c k (k=1,2,...,p) composition.

[0030] Each element of the input vector needs to be normalized before training, where each element is normalized to [-1,1].

[0031] The standard BP algorithm can be used here for the training of the above-mentioned MADALINE neural network.

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Abstract

The invention discloses a method and a device for constructing a MADALINE neural network based on sensitivity. The method comprises the following steps of selecting a big enough positive integer m as a hidden layer neuronal number, constructing a three-layer MADALINE neural network, and setting initial network parameters; utilizing a marked sample set to train the neural network until a certain given extremely small threshold value e is converged in a cost function to obtain a classifier through training; calculating the sensitivity of hidden layer neurons, and ranking from small to large according to the sensitivity; eliminating the hidden layer neuron with the minimum sensitivity to obtain the MADALINE neural network of a new structure; training the new MADALINE neural network on the basis of the original parameters by reusing the marked sample set; taking the network structure of the MADALINE neural network which has the minimum hidden layer neuronal number and is capable of converging as a final network structure, wherein the network of the network structure is the classifier for final output. According to the method and the device, the construction efficiency of the neural network can be effectively improved, and the performance of the MADALINE neural network is improved.

Description

technical field [0001] The present invention relates to a network construction method and device for MADALINE neural network design, in particular to a network construction method and device that can effectively improve the classification efficiency or regression efficiency of MADALINE neural network, and belongs to the field of machine learning in intelligent science and technology . Background technique [0002] When designing the MADALINE neural network classifier, how to determine the structure of the neural network is an important and critical step. Constructing a suitable network for specific problems is of great help to improve classification accuracy and generalization ability. Currently, three-layer neural networks are widely used. The literature has proved that the three-layer neural network can approach any continuous function when the number of neurons in the second layer (also known as the hidden layer and the middle layer) increases. In a specific applicatio...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/02G06K9/66
Inventor 储荣
Owner HOHAI UNIV
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