A neural observer with time-varying learning rate: analysis and applications.

In this paper, a reduced order neural observer (RONO) with a time-varying learning rate is proposed. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. A time-varying learning rate is designed in order to improve the learning of the neuronal network in presence of disturbances and parameter variations. This work includes the stability proof of the time-varying learning. The applicability of the developed observer is illustrated via simulations for a nonlinear anaerobic digestion process.

A new neural observer for an anaerobic bioreactor.

In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate variables of methanogenesis: biomass, substrate and inorganic carbon in a completely stirred tank reactor (CSTR). The recurrent high order neural network (RHONN) structure is based on the hyperbolic tangent as activation function. The learning algorithm is based on an extended Kalman filter (EKF). The applicability of the proposed scheme is illustrated via simulation. A validation using real data from a lab scale process is included. Thus, this observer can be successfully implemented for control purposes.

Host gender in parasitic infections of mammals: an evaluation of the female host supremacy paradigm.

A review of current literature on mammalian hosts' sexual dimorphism (SD) in parasitic infections revealed that (1) it is a scarcely and superficially studied biological phenomenon of considerable significance for individual health, behavior, and lifestyles and for the evolution of species; (2) there are many notable exceptions to the rule of a favorable female bias in susceptibility to infection; (3) a complex network of molecular and cellular reactions connecting the host's immuno-neuroendocrine systems with those of the parasite is responsible for the host-parasite relationship rather than just an adaptive immune response and sex hormones; (4) a lack of gender-specific immune profiles in response to different infections; (5) the direct effects of the host hormones on parasite physiology may significantly contribute to SD in parasitism; and (6) the need to enrich the reductionist approach to complex biological issues, like SD, with more penetrating approaches to the study of cause-effect relationships, i.e., network theory. The review concludes by advising against generalization regarding SD and parasitism and by pointing to some of the most promising lines of research.

Adaptive recurrent neural control for nonlinear system tracking.

We present a new indirect adaptive control law based on recurrent neural networks, which are linear on the input. For the identifier, we adapt a recently published algorithm to fit the neural network type used for identification; this algorithm ensures exponential stability for the identification error. The proposed controller is based on sliding mode techniques. Our main result, stated as a theorem, concerns tracking error asymptotic stability. Applicability of the proposed scheme is tested via simulations.

Nonlinear adaptive trajectory tracking using dynamic neural networks.

In this paper the adaptive nonlinear identification and trajectory tracking are discussed via dynamic neural networks. By means of a Lyapunov-like analysis we determine stability conditions for the identification error. Then we analyze the trajectory tracking error by a local optimal controller. An algebraic Riccati equation and a differential one are used for the identification and the tracking error analysis. As our main original contributions, we establish two theorems: the first one gives a bound for the identification error and the second one establishes a bound for the tracking error. We illustrate the effectiveness of these results by two examples: the second-order relay system with multiple isolated equilibrium points and the chaotic system given by Duffing equation.

Membranous glomerulonephritis in rheumatoid arthritis unrelated to gold, D-penicillamine or other connective tissue disease.

We report a 58-year-old woman with classical rheumatoid arthritis (RA) who developed a membranous glomerulonephritis (MGN). She had never been treated with gold or D-penicillamine; other connective tissue diseases as well as hepatitis B were excluded. We suggest that the responsible cause of MGN is RA.