Nonlinear system modeling based on constrained Volterra series estimates

A simple nonlinear system modeling algorithm designed to work with limited \emph{a priori }knowledge and short data records, is examined. It creates an empirical Volterra series-based model of a system using an $l_{q}$-constrained least squares algorithm with $q\geq 1$. If the system $m\left( \cdot \right)$ is a continuous and bounded map with a finite memory no longer than some known $\tau$, then (for a $D$ parameter model and for a number of measurements $N$) the difference between the resulting model of the system and the best possible theoretical one is guaranteed to be of order $\sqrt{N^{-1}\ln D}$, even for $D\geq N$. The performance of models obtained for $q=1,1.5$ and $2$ is tested on the Wiener-Hammerstein benchmark system. The results suggest that the models obtained for $q>1$ are better suited to characterize the nature of the system, while the sparse solutions obtained for $q=1$ yield smaller error values in terms of input-output behavior

Mean-Field-Type Games in Engineering

A mean-field-type game is a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile but also the joint distributions of state-action pairs. This article presents some engineering applications of mean-field-type games including road traffic networks, multi-level building evacuation, millimeter wave wireless communications, distributed power networks, virus spread over networks, virtual machine resource management in cloud networks, synchronization of oscillators, energy-efficient buildings, online meeting and mobile crowdsensing.Comment: 84 pages, 24 figures, 183 references. to appear in AIMS 201

Decentralized allocation of human capital and nonlinear growth

The standard two-sector growth model with physical and human capital characterizes a process of material accumulation involving simple dynamics; constant long run growth is observable when assuming conventional Cobb-Douglas production functions in both sectors. This framework is developed under a central planner scenario: it is a representative agent that chooses between consumption and capital accumulation, on one hand, and between allocating human capital to each one of the two sectors, on the other. We concentrate in this second choice and we argue that the outcome of the aggregate model is incompatible with a scenario where individual agents, acting in a market economy, are free to decide, in each time moment, how to allocate their human capital in order to produce goods or to create additional skills. Combining individual incentives, the effort of a central planner (i.e., government) to approximate the decentralized outcome to the optimal result and a discrete choice rule that governs the decisions of individual agents, we propose a growth framework able to generate a significant variety of long term dynamic results, including endogenous fluctuations.Endogenous growth; Human capital; Endogenous business cycles; Discrete choice; Nonlinear dynamics; Chaos