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In this paper, a model based on weighted extreme learning machine (weighted ELM) is proposed for visual tracking. The weighted ELM considers different class distributions both of the positive and negative classes, where extra weights are utilized in the framework. The proposed model simultaneously trains a certain number of weighted ELMs with different feature blocks. Moreover, the weighted multiple...
This paper presents a new neural-network method to describe the electromagnetic (EM) behavior at the interface between the substructures from an internally decomposed EM structure. A set of neural networks is used to represent the EM behavior of the substructure as seen from the interface. This allows EM coupling between substructures to be effectively represented. The method is developed in a finite-element...
In this paper, we present a new method for modeling the nonlinear transient behavior of I/O buffers in high-speed PCB design. The proposed method expands the existing StateSpace Dynamic Neural Network (SSDNN) into a more generalized and efficient technique for modeling nonlinear behavior of I/O buffers. A Multi-Layer Perceptron (MLP) neural network with multiple hidden layers is combined with the...
This paper presented a nonlinear voltage modeling procedure of a proton exchange membrane fuel cell (PEMFC) stack by neural networks based on particle swarm optimization (PSO). PEMFC stack is a complex nonlinear system which is hard to model by traditional ways. So neural networks based on particle swarm optimization (PSONN) was developed to identify a nonlinear PEMFC stack voltage model. In the paper,...
Computational fluid dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization. However, it normally takes very long time to solve such optimization problems due to the heavy computation load involved in CFD simulations, where a number of differential equations are to be solved. Some efforts have...
In this paper, a parallel automatic model generation (PAMG) technique is proposed to speedup the development of artificial neural network (ANN) models for microwave modeling. The automatic model generation (AMG) converts human based manual modeling into an automated computational process. AMG typically involves intensive computations in adaptive data sampling by repetitively driving detailed EM/physics/circuit...
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