Patent ID: 11948092
Assignee: NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A brain-inspired cognitive learning method based on a machine learning framework, comprising the following steps:
(1) online learning by the machine learning framework, comprising:
(1a) accepting a dynamic environment and a dynamic task from an outside world:
(1b) extracting, by a cognitive feature extraction module, features of the dynamic environment and the dynamic task, and transmitting the features to a radial basis function neural network coupled with a structure of back-propagation neural network (RBFNN-BPNN):
(1c) establishing, by RBFNN-BPNN a mapping relationship between the features obtained in step (1b) and algorithms and hyper-parameters in a memory module to obtain an online algorithm model and hyper-parameter combination;
(1d) calling, by the RBFNN-BPNN, the online algorithm model and hyper-parameter combination selected in step (1c) from an algorithm and hyper-parameter base in the memory module, and transmitting the online algorithm model and hyper-parameter combination to a learning network; and
(1e) executing, by a learning network module, the online algorithm model and hyper-parameter combination called in step (1d), executing a hyper-parameter and an algorithm in the dynamic environment and the dynamic task extracted in step (1b), obtaining an online learning result, and outputting the online learning result to the dynamic environment of the outside world;
(2) offline self-learning by the machine learnim1 framework comprising:
(2a) storing raw data of the dynamic environment and the dynamic task extracted in step (1b) of the online learning into a data base in the memory module, and storing the features of the dynamic environment and the dynamic task, the online algorithm model and hyper-parameter combination selected in step (1c), and the online learning result obtained in step (1e) as a new cognitive case into a cognitive case base in the memory module;
(2b) sampling a cognitive case in the cognitive case base;
(2c) establishing, by the RBFNN-BPNN, a mapping relationship between features of the cognitive case obtained in step (2b) and algorithms and hyper-parameters in the memory module, to obtain an offline algorithm model and hyper-parameter combination;
(2d) calling, by the RBFNN-BPNN, the offline algorithm model and hyper-parameter combination selected in step (2c) from the algorithm and hyper-parameter base in the memory module, and transmitting the offline algorithm model and hyper-parameter combination to the learning network;
(2e) executing, by the learning network based on the dynamic environment and the dynamic task in step (2b), the offline algorithm model and hyper-parameter combination obtained in step (2d), obtaining an offline learning result, and transmitting the offline learning result to a cognitive evaluation module;
(2f) transmitting, by the cognitive case base, a previous learning result of the cognitive case in step (2b) to the cognitive evaluation module;
(2g) comparing, by the cognitive evaluation module, the offline learning result of the cognitive case in step (2e) with the previous learning result of the cognitive case in step (2t″), transmitting a better learning result and the offline algorithm model and hyper-parameter combination that achieve the better learning result to the cognitive case base as an updated algorithm model, hyper-parameter combination, and performance, and updating the cognitive case in step (2b); and
(2h) retraining the RBFNN-BPNN according to an updated cognitive case base.