Deep extreme learning machine-based hazard source identification method

Abstract

The invention discloses a deep extreme learning machine-based hazard source identification method. A deep neural network adopted by the method consists of two parts: a deep structure module and a single-hidden layer neural network module. The method comprises the following steps of: dividing hazard source information into different domain classifications by utilizing SVM and inputting the different domain classifications into corresponding network modules; carrying out an S-ELM algorithm on each network module to obtain each pre-identification result of the deep network; combining the pre-identification results of the deep network to serve as an input of a top neural network; calculating an initial hidden layer output and an output weight of a single-hidden layer ELM according to an ELM algorithm and an excitation function; determining a final input weight, a hidden layer feature space and an output weight of the network according to an improved counter propagation algorithm; and finally obtaining a hazard source identification result. The identification method disclosed by the invention can be used for improving the flexibility of the hazard source identification, decreasing the rapid expansion of empirical data, improving the utilization rate of experiential knowledge and relieving the internal memory pressure during high-dimensional data training.

Description

technical field [0001] The invention belongs to the technical field of information perception and identification, and in particular relates to a hazard identification method based on a deep extreme learning machine. Background technique [0002] With the rapid development of civil aviation, the pressure on air traffic safety management has increased sharply. Quickly and accurately discovering the hazards in the air traffic control system and controlling them accurately play an important role in improving the safety of air traffic control. [0003] At present, hazard identification technology can be roughly divided into three types. The first is the manual analysis method, which mainly relies on experience and knowledge, combined with relevant analysis methods provided by the Aviation Administration, but cannot liberate manpower to solve more complex problems. The second method is the computer-aided method, which assists manual analysis or implements the analysis process of...

AI Technical Summary

Problems solved by technology

The first is the manual analysis method, which mainly relies on experience and knowledge, combined with relevant analysis methods provided by the Aviation Administration, but cannot liberate manpower to solve more complex problems

The second method is the computer-aided method, which assists manual analysis or realizes the analysis process of related methods through computer-built system models, which reduces the consumption of manpower, but cannot make full use of existing experience and knowledge

The third method is an intelligent hazard identification method represented by an expert system. At present, the core technology of this method is mainly a case-based reasoning method. This method can effectively use empirical knowledge, but there are problems of expansive experience database and poor flexibility.

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