A new kernel-based approach for overparameterized Hammerstein system identification

In this paper we propose a new identification scheme for Hammerstein systems, which are dynamic systems consisting of a static nonlinearity and a linear time-invariant dynamic system in cascade. We assume that the nonlinear function can be described as a linear combination of $p$ basis functions. We reconstruct the $p$ coefficients of the nonlinearity together with the first $n$ samples of the impulse response of the linear system by estimating an $np$-dimensional overparameterized vector, which contains all the combinations of the unknown variables. To avoid high variance in these estimates, we adopt a regularized kernel-based approach and, in particular, we introduce a new kernel tailored for Hammerstein system identification. We show that the resulting scheme provides an estimate of the overparameterized vector that can be uniquely decomposed as the combination of an impulse response and $p$ coefficients of the static nonlinearity. We also show, through several numerical experiments, that the proposed method compares very favorably with two standard methods for Hammerstein system identification.Comment: 17 pages, submitted to IEEE Conference on Decision and Control 201

Kernel-based system identification from noisy and incomplete input-output data

In this contribution, we propose a kernel-based method for the identification of linear systems from noisy and incomplete input-output datasets. We model the impulse response of the system as a Gaussian process whose covariance matrix is given by the recently introduced stable spline kernel. We adopt an empirical Bayes approach to estimate the posterior distribution of the impulse response given the data. The noiseless and missing data samples, together with the kernel hyperparameters, are estimated maximizing the joint marginal likelihood of the input and output measurements. To compute the marginal-likelihood maximizer, we build a solution scheme based on the Expectation-Maximization method. Simulations on a benchmark dataset show the effectiveness of the method.Comment: 16 pages, submitted to IEEE Conference on Decision and Control 201

Stabilization of Food Colloids: The Role of Electrostatic and Steric Forces

The role that some forces exert on food colloid stability is discussed. The focus is on the combination of different energy terms, determining particle-particle attraction or repulsion. The forces are relevant in dispersion stabilization and macroscopic phase separation. The observed features depend on the energies at work and colloid concentration. Examples deal with food manipulations giving cheese, yogurt, and mayonnaise. All products result from the overlapping of forces jointly leading to aggregation or phase separation in foods. The combination of attractive, van der Waals (vdW), and repulsive, double-layer (DL) forces results in the dominance of aggregation or dispersion modes, depending on the particle concentration, on the force amplitude, and on their decay length. DL and vdW forces are at the basis of Derjaguin-Landau-Verwey-Overbeek (DLVO) theory on colloid stability. That approach is modified when these forces, jointly operating in bio-based colloids, overlap with steric stabilization and depletion modes. Steric effects can be strongly dispersive even at high ionic strength, despite this is rather counterintuitive, when depletion ones favor the nucleation in a single phase

Surfactant Mixtures: Performances vs. Aggregation States

Surfactants, Mixtures, interaction

Colloid Stability Influences on the Biological Organization and Functions

It is common to entities having sizes in the nano/micro-scale range be that, real or bio-intended systems, to undergo the action of many different forces, imparting them colloid stability. Ubiquitary electrostatic contributions, sometimes dominant, may overlap with steric stabilization ones; their combination effectively takes place in most cases. The two effects are jointly responsible, for instance, for the control of many phenomena such as: adhesion onto cells of alien agents, cellular separation during morpho-functional evolution, uptake of exogenous materials into cells and tissues. We evidence here, how the combination of these forces operates, and indicate the procedures leading to their effectiveness, when required for purposes inherent to biomimicry