A novel algorithm based on Kalman filtering is developed for the learning of a layered neural network. The problem of adjusting the weight can be regarded as that of estimating a signal state vector of a linear process. The proposed algorithm, though computationally complex, has an adaptively varying learning rate, while the back-propagation algorithm has constant learning rate. Some experiments conducted for XOR and auto-associative image compression problems have shown that the proposed learning algorithm usually converges in a few iterations and the error is comparable to that of the well-known back-propagation algorithm.

There are many knowledge entities in sci-tech intelligence resources. Extracting these knowledge entities is of great importance for building knowledge networks, exploring the relationship between knowledge, and optimizing search engines. Many existing methods, which are mainly based on rules and traditional machine learning, require significant human involvement, but still suffer from unsatisfactory extraction accuracy. This paper proposes a novel approach for knowledge entity extraction based on BiLSTM and conditional random field (CRF).A BiLSTM neural network to obtain the context information of sentences, and CRF is then employed to integrate global label information to achieve optimal labels. This approach does not require the manual construction of features, and outperforms conventional methods. In the experiments presented in this paper, the titles and abstracts of 20,000 items in the existing sci-tech literature are processed, of which 50,243 items are used to build benchmark datasets. Based on these datasets, comparative experiments are conducted to evaluate the effectiveness of the proposed approach. Knowledge entities are extracted and corresponding knowledge networks are established with a further elaboration on the correlation of two different types of knowledge entities. The proposed research has the potential to improve the quality of sci-tech information services.