Abstract:In electro-hydraulic system (EHS), uncertain nonlinearities such as some hydraulic parametric uncertainties and external load disturbance often degrade the output dynamic performance. To address this problem, a prescribed performance constraint (PPC) control method is adopted in EHS to restrict the tracking position error of the cylinder position to a prescribed accuracy and guarantee the dynamic and steady position response in a required boundedness under these uncertain nonlinearities. Furthermore, a dynamic surface is designed to avoid the explosion of complexity due to the repeatedly calculated differentiations of the virtual control variables derived in backstepping. The effectiveness of the proposed controller has been verified by a comparative results.
Traditional physical education methods are unable to meet this requirement due to the practical nature of sports skill teaching. As a result, as the times demanded, the flipped classroom based on neural network technology arose. It has the potential to not only promote the modernization of physical education but also to ensure that it has a positive educational impact. This is a mode of instruction. Furthermore, colleges and universities are increasingly focusing on college students’ overall quality development. A method for predicting college students’ sports performance using a particle swarm optimization neural network is proposed to accurately predict sports performance and provide a reliable analysis basis for the establishment of sports teaching goals. Neural networks are used in the model. The particle swarm optimization algorithm optimizes the variance and weights of the neural network to improve the accuracy of college students’ sports performance predicted by the neural network by updating the particle position and speed through the two extreme values of individual extreme values and global extreme values. Teachers always play the role of the facilitator and helper in the teaching process, which realizes the transformation of teachers’ and students’ self-positioning, allows students to better play the lead role, and stimulates students’ interest in learning.
This paper presents a Bayesian decision framework that performs automatic story segmentation based on statistical modeling of one or more lexical chain features. Automatic story segmentation aims to locate the instances in time where a story ends and another begins. A lexical chain is formed by linking coherent lexical items chronologically. A story boundary is often associated with a significant number of lexical chains ending before it, starting after it, as well as a low count of chains continuing through it. We devise a Bayesian framework to capture such behavior, using the lexical chain features of start, continuation and end. In the scoring criteria, lexical chain starts/ends are modeled statistically with the Weibull and uniform distributions at story boundaries and non-boundaries respectively. The normal distribution is used for lexical chain continuations. Full combination of all lexical chain features gave the best performance (F1=0.6356). We found that modeling chain continuations contributes significantly towards segmentation performance.
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