A perceptron network data classification method based on an AdaBoost algorithm

A technology of network data and classification methods, applied in the direction of instruments, computing, computer parts, etc., can solve the problems of costing a lot of money and time, and achieve the effect of strong efficiency, improved classification accuracy, and simple model structure.

Inactive Publication Date: 2019-05-07
胡燕祝
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Problems solved by technology

Therefore, the technology put into use must ensure a high accuracy rate and a simple algorithm process, because if the model is too compl

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  • A perceptron network data classification method based on an AdaBoost algorithm
  • A perceptron network data classification method based on an AdaBoost algorithm
  • A perceptron network data classification method based on an AdaBoost algorithm

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[0035] specific implementation plan

[0036] The present invention will be described in further detail below through examples of implementation.

[0037] Taking the fault diagnosis of refrigerator charging as an example, the selected data set comes from a multi-line refrigeration charging experiment, and the equipment consists of 3 indoor units and 1 outdoor unit. The data set includes a total of 18,000 data records, and the data includes 22 characteristic variables such as outdoor ambient temperature, local machine distribution capacity, compressor operating frequency, fan operating frequency, compressor shell top temperature, and compressor exhaust temperature. There are 3 types of failure states: insufficient refrigerant charge, normal refrigerant charge and excessive refrigerant charge. The data set is divided into two parts, the training set and the test set. The number of samples in the training set is 14400, and the number of samples in the test set is 3600.

[0038] ...

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Abstract

The invention relates to a perceptron network data classification method based on an AdaBoost algorithm. The perceptron network data classification method is characterized by comprising the followingsteps: (1) determining a training sample set; (2) calculating the output weight of the sample set in the weak classifier; (3) determining a classifier weight vector; (4) determining input of a sensorand an activation function; (5) calculating a weighted error rate; (6) updating the sample weight; (7) updating a classifier weight vector; And (8) taking a test set sample as an input, and sending the input to a sensor for classification to obtain a classification result. According to the method, the network connection weight of the sensor is updated and iterated by using the idea of integrated learning in the AdaBoost algorithm, and a plurality of weak classifiers are integrated into a strong classifier, so that the classification accuracy of the model is improved. According to multiple groups of data experiment results, the classification method for improving the timeliness of the model on the basis of ensuring the classification accuracy is provided for numerical classification.

Description

technical field [0001] The invention relates to the fields of machine learning and data mining, and mainly relates to a method for classifying data. Background technique [0002] At present, for data classification problems, most technologies can achieve high accuracy, but the classification process is unstable and the robustness is poor, and some models are too complex, resulting in too long training time. Although some models have simple algorithms and fast training time, they cannot guarantee the classification accuracy. In classic machine learning classification tasks, data features are generally extracted in advance. After extracting many features, correlation analysis must be performed on these features to find the features that best represent characters and remove features that are irrelevant to classification. However, with the rapid development of the computer Internet and the exponential growth of massive data, how to efficiently extract useful data features is ve...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 胡燕祝王松
Owner 胡燕祝
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