In this Chapter, we provide the definitions, notions and examples relevant for the analysis of the dynamical systems of interest to us in the remainder of this book. We start with with a description of dynamical systems and we provide a taxonomy. Then, we define continuous-time dynamical systems...

In this chapter, the Logistic Map is taken as the example demonstrating the generic stability properties of fixed points and limit cycles, in dependence of the strength of nonlinearity. To identify attracting periodic orbits, we use the Schwarz derivative. The chapter ends with an application of...

Many dynamical systems depend on parameters. One may expect that small variations of the parameters produce no significant changes in the orbits. As was shown in Chap. 3 for the Logistic Map, even in simple cases, there exist critical values such that, moving the parameters through them, the...

This chapter consists of four sections. Section 5.1 reiterates with the Hopf bifurcation theorem. Section 5.2 provides two specific systems that generate chaotic motions by means of numerical simulations. Section 5.3 explains Shilnikov’s theorem. Finally, Sect. 5.4 treats the emergence of...

In this chapter, we first precise the concept of dynamical systems, and then we introduce the concept of chaos, which is characterized by a sensitive dependence on initial conditions. To quantify this, dynamical (Lyapunov exponents) and probabilistic (dimensions) measures are introduced.

In this chapter, we introduce the concept of the embedding dimension, as the smallest topological dimension required to ensure that an object described by simpler (often scalar) time series can be embedded in a higher topological dimension.

In this chapter, I present a specific view of signal processing. I provide the basic definitions regarding what - according to our view - signal processing should be seen as, and I elucidate in this perspective the meaning of a selection of relevant algorithms and approaches.

In this chapter, we first explain what we mean by a signal, and then we describe some characteristics such as energy, frequency, phase, power spectrum, etc. We show how to analyse it by the means of spectral analysis and Fourier transform. Moreover, as the Fourier transform does not provide any...

Complex systems are characterized by deterministic laws (which often may be hidden) and randomness. A tool to analyse those systems is recurrence quantification analysis (RQA). RQA does not rely on any sort of assumption of stationarity and is not sensitive to singularities and transitions. It...

In this chapter, we outline the reasons why economics has been concerned with non-linear dynamics, with a particular focus on business cycles and on economic growth. Using varying perspectives, we discuss the salient historical mathematical approaches to the problem and the results that were...