计算机科学
人工神经网络
非线性系统
人工智能
函数依赖
利用
机器学习
基础(线性代数)
算法
数据挖掘
数学
几何学
关系数据库
计算机安全
量子力学
物理
作者
Aniruddha Rajendra Rao,Matthew Reimherr
标识
DOI:10.1080/10618600.2023.2165498
摘要
We introduce a new class of nonlinear models for functional data based on neural networks. Deep learning has been very successful in nonlinear modeling, but there has been little work done in the functional data setting. We propose two variations of our framework: a functional neural network with continuous hidden layers, called the Functional Direct Neural Network (FDNN), and a second version that uses basis expansions and continuous hidden layers, called the Functional Basis Neural Network (FBNN). Both are designed explicitly to exploit the structure inherent in functional data. To fit these models we derive a functional gradient based optimization algorithm. The effectiveness of the proposed methods in handling complex functional models is demonstrated by comprehensive simulation studies and real data examples. Supplementary materials for this article are available online.
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