Systematic construction of continuous-time neural networks for linear dynamical systems

TL;DR

This paper brings forth a novel neural architecture paradigm featuring horizontal hidden layers and provides insights into why employing conventional neural architectures with vertical hidden layers may not be favorable, and provides an upper bound on the numerical errors of the neural networks.

Abstract

Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation through a high-dimensional hyper-parameter space. In this paper, we discuss a systematic approach to constructing neural architectures for modeling a subclass of dynamical systems, namely, Linear Time-Invariant (LTI) systems. We use a variant of continuous-time neural networks in which the output of each neuron evolves continuously as a solution of a first-order or second-order Ordinary Differential Equation (ODE). Instead of deriving the network architecture and parameters from data, we propose a gradient-free algorithm to compute sparse architecture and network parameters directly from the given LTI system, leveraging its properties. We bring forth a novel neural architecture paradigm featuring horizontal hidden layers and provide insights into why employing conventional neural architectures with vertical hidden layers may not be favorable. We also provide an upper bound on the numerical errors of our neural networks. Finally, we demonstrate the high accuracy of our constructed networks on three numerical examples.

Figures (2)

• Figure 1: Example figure using external image files.
• Figure 2: Example PGFPLOTS figure.

Theorems & Definitions (5)

• Theorem 6.1: Mean Value Theorem
• Corollary 6.2
• Proof 1
• Claim 6.3
• Proof 2: Proof of main theorem