Titlebook: Neural Computing for Advanced Applications; First International Haijun Zhang,Zhao Zhang,Tianyong Hao Conference proceedings 2020 Springer

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多嘴多舌

Adaptive Neural Network Control for Double-Pendulum Tower Crane Systems,ch makes the model more complicated and most existing control methods inapplicable. Additionally, most available control methods for tower cranes need to linearize the original dynamics and require exact knowledge of system parameters, which may degrade the control performance significantly and make

恶臭

Generalized Locally-Linear Embedding: A Neural Network Implementation,encoders have achieved great success in learning data representation via the deep neural networks (DNN). It is interesting to get the best of both worlds by implementing LLE with DNN. To this end, we introduce an extra fully-connected layer whose weight works as a reconstruction coefficient (i.e., r

残酷的地方

Semi-supervised Feature Selection Using Sparse Laplacian Support Vector Machine,lly applied to semi-supervised learning. However, LapSVM cannot be directly applied to feature selection. To remedy it, we propose a sparse Laplacian support vector machine (SLapSVM) and apply it to semi-supervised feature selection. On the basis of LapSVM, SLapSVM introduces the .-norm regularizati

Acupressure

慢慢冲刷

Coordinative Hyper-heuristic Resource Scheduling in Mobile Cellular Networks,ular networks. The task of this problem is to minimize the required bandwidth to satisfy diverse channel demand from all micro cellular, while without interference violation. Based on the undirected weighted graph generated by each network topology, six problem-related low-level heuristics are const

阻止

Shuttle

Conflagration

Discriminative Subspace Learning for Cross-view Classification with Simultaneous Local and Global Action existed in cross-view data is that the data of the different views from the same semantic space are further than that within the same view but from different semantic spaces. To solve this special phenomenon, we design a novel discriminative subspace learning model via low-rank representation.

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