.png){kind=icon}
返回
Heterogeneous Multilayer Generalized Operational Perceptron
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2019.2914082.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational perceptron (GOP) was proposed to extend the conventional perceptron model by defining a diverse set of neuronal activities to imitate a generalized model of biological neurons. Together with GOP, a progressive operational perceptron (POP) algorithm was proposed to optimize a predefined template of multiple homogeneous layers in a layerwise manner. In this paper, we propose an efficient algorithm to learn a compact, fully heterogeneous multilayer network that allows each individual neuron, regardless of the layer, to have distinct characteristics. Based on the complexity of the problem, the proposed algorithm operates in a progressive manner on a neuronal level, searching for a compact topology, not only in terms of depth but also width, i.e., the number of neurons in each layer. The proposed algorithm is shown to outperform other related learning methods in extensive experiments on several classification problems.
Keyword:
Neurons
Biological neural networks
Network topology
Learning systems
Topology
Computational modeling
Nonhomogeneous media
Architecture learning
feedforward network
generalized operational perceptron (GOP)
progressive learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
On the performance of air-based solar heating systems utilizing phase-change energy storage.png)
Energy
EnergyIF0
PREAI
分享收藏分享收藏分享收藏分享收藏
查看更多内容