Abstract
This document focuses attention on the fundamental solution of an autonomous linear retarded functional differential equation (RFDE) along with its supporting cast of actors: kernel matrix, characteristic matrix, resolvent matrix; and the Laplace transform. The fundamental solution is presented in the form of the convolutional powers of the kernel matrix in the manner of a convolutional exponential matrix function. The fundamental solution combined with a solution representation gives an exact expression in explicit form for the solution of an RFDE. Algebraic graph theory is applied to the RFDE in the form of a weighted loop-digraph to illuminate the system structure and system dynamics and to identify the strong and weak components. Examples are provided in the document to elucidate the behavior of the fundamental solution. The paper introduces fundamental solutions of other functional differential equations.
Highlights
This paper examines and characterizes the fundamental matrix solution for the n-vector autonomous linear retarded functional differential equation (RFDE)
This paper has extended the expression for and the treatment of the fundamental solution for autonomous linear RFDE from scalar to n-vector, in the following ways: The fundamental solution is presented in the form of a convolutional exponential matrix function
The paper demonstrates how RFDE analysis can be conducted through an interplay of the RFDE actors and tools (Laplace transform and solution representation)
Summary
This paper examines and characterizes the fundamental matrix solution for the n-vector autonomous linear retarded functional differential equation (RFDE). The use of measures in the kernel matrix of the RFDE in Equation (1) is chosen over the alternatives of (i) distributions and (ii) functions of normalized bounded variation. This choice is mainly a matter of style.
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