Evolutionary neural network and weight updating method and device for intelligent evaluation of controller
A neural network and controller technology, applied in the field of air traffic control, can solve the problems of poor adaptability of the controller evaluation system, and achieve the effect of increasing the ability of optimization and self-adaptation
Pending Publication Date: 2022-05-13
中国民用航空局空中交通管理局
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[0007] The embodiment of the present application provides an evolutionary neural network, an intelligent evaluation method and device for intelligent evaluation of controllers, so as to at least solve the technical problem of poor adaptability of the existing controller evaluation system
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[0039] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.
[0040] It should be noted that the steps shown in the flowcharts of the accompanying drawings may be performed in a computer system, such as a set of computer-executable instructions, and that although a logical order is shown in the flowcharts, in some cases, The steps shown or described may be performed in an order different than here.
[0041] The specific workflow in this patent embodiment is as follows figure 1 Shown: By using the regulatory intelligence simulator and quantitative indicators representing the characteristics of regulatory work, the weights of the indicators are calculated in the following three steps:
[0042]In the first step, use a backpropagation neural network su...
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The invention discloses an evolutionary neural network for controller intelligent evaluation and a weight updating method and device, and the structure of the evolutionary neural network comprises the following layers which are sequentially arranged: a first input layer which comprises a plurality of nodes corresponding to secondary indexes; the second input layer comprises five node surfaces corresponding to the first-level indexes; numerical values in nodes of the artificial weight layer are obtained after data in nodes in the second input layer and artificial empowerment are multiplied; an NEAT neural network hidden layer; the first output layer and the second output layer comprise five output nodes corresponding to the first-level indexes and one comprehensive result node; the evolutionary neural network takes computer operation recorded in the control intelligent simulator as input and takes a corresponding scoring result as output to realize training; wherein the weight of the initial artificial empowerment of the artificial weight layer during training is obtained through back propagation neural network training.
Description
technical field [0001] This application relates to the field of air traffic control, in particular to a weight optimization method of an intelligent evaluation system for controllers based on an evolutionary neural network. Background technique [0002] The controller intelligent evaluation system is an intelligent evaluation system based on the control simulator, and it is an important auxiliary equipment for controller evaluation in various scenarios and stages. Not only can it be used to simulate dangerous / emergency / far beyond the general workload scenarios that cannot or are extremely unlikely to occur in actual work scenarios, to achieve the purpose of testing the controller's general upper limit of capabilities and emergency handling capabilities; it can also be used as teaching equipment, Provide a good simulation environment for new control trainees to familiarize themselves with the basic control workflow, work scenarios, and working methods. [0003] During the co...
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IPC IPC(8): G06N3/04G06N3/08G06Q10/06G06Q50/20
CPCG06N3/04G06N3/082G06N3/084G06Q10/06393G06Q50/2057
Inventor 冯铮沈志远陈章齐
Owner 中国民用航空局空中交通管理局