In order to enhance the presented synchronization scheme level, this paper deals with the synchronization problem of a class of bidirectional associative memory (BAM) neural networks with delays. Using the drive-response concept, a feedback control law is derived to achieve the state synchronization of two identical BAM neural networks. Moreover, based on the Lyapunov stability method and the Halanay inequality lemma, two delay-independent sufficient exponential synchronization conditions are derived. The results in the present study provide new insights into the exponential synchronization of BAM neural networks. An example is given to show the effectiveness of the obtained results.
The rapid development of e-commerce has prompted the emergence of massive product reviews. Quickly grasping the emotional tendencies in e-commerce product reviews has become an effective way for businesses to improve their problems. Therefore, based on Bert and GUP, the research proposes Bert BiGRU deep learning algorithm, and verifies its effectiveness in the emotional analysis of e-commerce product reviews. The experimental results show that Bert BiGRU algorithm has the highest accuracy of 95.51% among the seven algorithms. In addition, Bert BiGRU algorithm has achieved good training effect after only eight times of training. In addition, Bert BiGRU algorithm has been used in practice to analyze the emotional tendencies in four mobile phone reviews, which shows high reliability. In a word, Bert BiGRU algorithm has high performance and low cost. It has high practicability in the emotional analysis of e-commerce product reviews.
The on-the-road fault diagnosis of the urban rail train passenger compartment door is a weak field in the world research. At present, most of the fault diagnosis and monitoring models for door systems are based on the analysis of historical data. Under the background of continuous development and innovation of railroad crossing equipment, it is urgent to study the model of door system suitable for online monitoring and fault diagnosis. The modeling method combining SDG(signed directed graph) diagram and Petri net is adopted. The Petri net with improved conditional fuzzy time constraint is the first layer, and the SDG diagram is the second layer. Through the dynamic simulation and concurrent processing capability of Petri net, the dynamic process simulation of the system is carried out. At the same time, the SDG map and the Petri net are connected by means of standard tables; The SDG diagram is used to construct a hazard identification and fault mining for the causal relationship between related variables in a certain state of the library. Aiming at the urban rail passenger room plug door system, the model is established and the online safety monitoring hidden danger mining process of the model method in the urban rail plug door is analyzed.
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