7 resultados para parameter learning process

em Chinese Academy of Sciences Institutional Repositories Grid Portal


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This paper explores the potential of the piecewise linear vibration absorber in a system subject to narrow band harmonic loading. Such a spring is chosen because the design of linear springs is common knowledge among engineers. The two-degrees-of-freedom system is solved by using the Incremental Harmonic Balance method, and response aspects such as stiffness crossing frequency and jump behaviour are discussed. The effects of mass, stiffness, natural frequency ratios, and stiffness crossing positions on the suppression zone are probed. It is shown that a hardening absorber can deliver a wider bandwidth than a linear one over a range of frequencies. The absorber parameters needed to produce good designs have been determined and the quality of the realized suppression zone is discussed. Design guidelines are formulated to aid the parameter selection process.

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This paper presents an two weighted neural network approach to determine the delay time for a heating, ventilating and air-conditioning (HVAC) plan to respond to control actions. The two weighted neural network is a fully connected four-layer network. An acceleration technique was used to improve the General Delta Rule for the learning process. Experimental data for heating and cooling modes were used with both the two weighted neural network and a traditional mathematical method to determine the delay time. The results show that two weighted neural networks can be used effectively determining the delay time for AVAC systems.

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This paper presents an multi weights neurons approach to determine the delay time for a Heating ventilating and air-conditioning (HVAC) plan to respond to control actions. The multi weights neurons is a fully connected four-layer network. An acceleration technique was used to improve the general delta rule for the learning process. Experimental data for heating and cooling modes were used with both the multi weights neurons and a traditional mathematical method to determine the delay time. The results show that multi weights neurons can be used effectively determining the delay time for HVAC systems.

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针对Internet多机器人系统中存在的操作指令延迟、工作效率低、协作能力差等问题,提出了多机器人神经元群网络控制模型。在学习过程中,来自不同功能区域的多类型神经元连接形成动态神经元群集,来描述各机器人的运动行为与外部条件、内部状态之间复杂的映射关系,通过对内部权值连接的评价选择,以实现最佳的多机器人运动行为协调。以互联网足球机器人系统为实验平台,给出了学习算法描述。仿真结果表明,己方机器人成功实现了配合射门的任务要求,所提模型和方法提高了多机器人的协作能力,并满足系统稳定性和实时性要求。

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Since the 19th century, people have long believed that the function of cerebellum was restricted to fine motor control and modulation. In the past two decades, however, more and more studies challenged this traditional view. While the neuroanatomy of the cerebellum from cellular to system level has been well documented, the functions of this neural organ remain poorly understood. This study, including three experiments, attempted to further the understanding of cerebellar functions from different viewpoints. Experiment One used the parametric design to control motor effects. The activation in cerebellum was found to be associated with the difficulty levels of a semantic discrimination task, suggesting the involvement of the cerebellum in higher level of language functions. Moreover, activation of the right posterior cerebellum was found to co-vary with that of the frontal cortex. Experiment Two adopted the cue-go paradigm and event-related design to exclude the effects of phonological and semantic factors in a mental writing task. The results showed that bilateral anterior cerebellum and cerebral motor regions were significantly activated during the task and the hemodynamic response of the cerebellum was similar to those of the cerebral motor cortex. These results suggest that the cerebellum participates in motor imagination during orthographic output. Experiment Three investigated the learning process of a verb generation task. While both lateral and vermis cerebellum were found to be activation in the task, each was correlated a separate set of frontal regions. More importantly, activations both in the cerebellum and frontal cortex decreased with the repetition of the task. These results indicate that the cerebellum and frontal cortex is jointly engaged in some functions; each serves as a part of a single functional system. Taken these findings together, the following conclusions can be drawn: 1.The cerebellum is not only involved in functions related to speech or articulation, but also participates in the higher cognitive functions of language. 2.The cerebellum participates in various functions by supporting the corresponding regions in cerebral cortex, but not directly executes the functions as an independent module. 3.The anterior part of cerebellum is related to motor functions, whereas the posterior part is involved in cognitive functions. 4.While the motor functions rely on the engagement of both sides of the cerebellar hemispheres, the higher cognitive functions mainly depend on the right cerebellum.

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This dissertation systematically depicted and improved the application of Independent Component Analysis (ICA) to Functional Magnetic Resonance Imaging (fMRI), following the logic of verification, improvement, extension, and application. The concept of “reproducibility” was the philosophy throughout its four concluded studies. In the “verification” study, ICA was applied to the resting-state fMRI data, verified the resultant components with reproducibility, and examined the consistency of the results from ICA and traditional “seed voxel” method. At the meantime, the limitation of ICA application on fMRI data analysis was presented. In the “improvement” study, an improved ICA algorithm based on reproducibility, RAICAR, was developed to aid some of the limitations of ICA application. RAICAR was able to rank ICA components by reproducibility, determine the number of reliable components, and obtain more stable results. RAICAR provided useful tools for validation and interpretation of ICA results. In the “extension” study, RAICAR as well as the concept of “reproducibility” was extended to multi-subject ICA analysis, and gRAICAR algorithm was developed. gRAICAR allows some variation across subjects, examining common components among subjects. gRAICAR is also capable to detect potential subject grouping on some components. It is a new way for exploratory group analysis on fMRI. In the “application” study, two newly developed methods, RAICAR and gRAICAR, were used to investigate the effect of early music training on the brain mechanism of memory and learning. The results showed brain mechanism difference in memory retrieval and learning process between two groups of subjects. This study also verified the usefulness and importance of the new methods.

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This study designed tow experiments to explore the effect of two presentation forms(liner presentation and concept-map navigation presentation)on the student's learning process. Using the method of protocal analysis and the learning path records of these students, the author of this paper further analysed the information-processing process of these students. The main results showed as follows: (1) In the initial study phase, the main effects of both the presentation form and the learner type were obvious, and the interaction effect of these two variables was also obvious. Contrasting with the liner presentation form, the concept map navigation form interfered with the learning process of the student, especially the learning-disabled students. (2) There was a significant difference between excellent students and learning-disabled learning-disabled on self explanations amount. Excellent students produced more self- explanations than learning-disabled students, especially on two phases of learning from the the example and the conclusion. (3) Under the same learning path, the main effect of the learner type variable was obvious, the main effect of the presentation form and interaction effect of these two variables weren't obvious. But the liner presentation grouped still acquired a better result than the concept-map navigation presentation groupe.