This paper presents a new method for initializing weights in a Feedforward Neural Network (FNN) with a single hidden layer combined with a constructive approach to define the number of hidden units associated with the best classification performance. The strategy consists of defining an initial ...
In this paper, we propose a multi-criteria decision making based architecture selection algorithm for single-hidden layer feedforward neural networks trained by extreme learning machine. Two criteria are incorporated into the selection process, i.e., training accuracy and the Q-value estimated by ...
Learning in neural networks has attracted considerable interest in recent years. Our focus is on learning in single hidden-layer feedforward networks which is posed as a search in the network parameter space for a network that minimizes an additive error function of statistically independent examples...
The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed ...
We also discussed the performance of GRN construction based on another two well-known attention techniques, including the scaled-dot product attention70 and the additive attention based on a single-layer feedforward neural network72, and provided these schemes as additional options in our package (Su...
among others48. We also used the mini-batch training strategy that randomly trains a small proportion of the samples in each iteration48. We additionally have tried applying recent deep learning techniques on the training feed-forward neural network, such as batch normalization and dropout, and fou...
与SSD相似,RefineDet基于feed-forward convolutional network产生一个固定数目的bounding boxes 和 表示这些box里的objects属于不同class的score, 后面接了non-maximum suppression 产生最后的结果. RefineDet 由两个 inter-connected modules组成:ARM 和 ODM. 这两个让performance比 two-stage方法更好并且有one-stage的效率...
Recently convolutional neural network (CNN) based SR algorithms have shown excellent performance. In Wang et al. [58] the authors encode a sparse representation prior into their feed-forward network architecture based on the learned iterative shrinkage and thresholding algorithm (LISTA) [22]. Dong ...
et al. Leveraging chromatin accessibility for transcriptional regulatory network inference in T helper 17 cells. Genome Res. 29, 449–463 (2019). Bengio, Y. & Glorot, X. Understanding the difficulty of training deep feed forward neural networks. In Proc. 13th International Conference on ...
4) multilayer feedforward neural network 多层前向神经网络 1. In a low-cost strapdown inertial navigation system(SINS),amultilayer feedforward neural network(NN) was designed to compensate the gyros asymmetry dynamic errors which caused attitude drift in rate oscillation. ...