Baseline pathological data of the wedge clam Donax trunculus from the Tyrrhenian Sea (Mediterranean Basin)

In recent years, a collapse in Donax trunculus fishing yields has occurred in the Tyrrhenian Sea (Mediterranean Basin). There is little information available on the impact disease may have had on D. trunculus populations. For the first time, a pathological survey was performed on the natural beds of the bivalve on the Campania and Lazio coasts, western Italy. Detected pathogens and related diseases were analysed, and their prevalence and mean intensity values were calculated. Viral particles, Chlamydia-like organisms, ciliates, coccidians, microcells and trematodes were observed. An unknown ciliate was linked to severe inflammatory and necrotic lesions in the digestive gland. Metacercariae of the trematode Postmonorchis sp. were also strongly represented in almost all samples, reaching high levels of infection; however, none of the pathogens described required the World Organisation for Animal Health to be notified. Initial results indicated that further surveys related to environmental data are necessary in order to assess the relevance of these early observations in managing the declining D. trunculus population in the Tyrrhenian Sea.postprin

Hierarchical intelligent sliding mode control: Application to stepper motors

In this paper, one method of robust control based on sliding mode and fuzzy logic techniques is presented. It combines hierarchical high gain control with fuzzy logic to design a discontinuous control for nonlinear multivariable systems in order to eliminate chattering in presence of disturbances. The proposed approach is then used to design a robust controller for a stepper motor. Simulation results are presented to illustrate the applicability of the approach

Intrastriatal injection of choline accelerates the acquisition of positively rewarded behaviors

The ultimate goal of control engineering is to implement an automatic system that could operate with increasing independence from human actions in an unstructured and uncertain environment. Such a system may be named autonomous or intelligent. It would need only to be presented with a goal and would achieve its objective by learning through continuous interaction with its environment through feedback about its behavior. One class of models that has the capability to implement this learning is the artificial neural networks. Indeed, the neural morphology of the nervous system is quite complex to analyze. Nevertheless, simplified analogies have been developed, which could be used for engineering applications. Based on these simplified understandings, artificial neural networks are built. An artificial neural network is a massively parallel distributed processor, inspired from biological neural networks, which can store experimental knowledge and makes it available for use. An artificial neural network consists of a finite number of neurons (structural element), which are interconnected to each other. It has some similarities with the brain, such as knowledge is acquired through a learning process and interneuron connectivity named as synaptic weights are used to store this knowledge, among others. " 2008 Springer-Verlag Berlin Heidelberg.",,,,,,"10.1007/978-3-540-78289-6_1",,,"http://hdl.handle.net/20.500.12104/42361","http://www.scopus.com/inward/record.url?eid=2-s2.0-46949105579&partnerID=40&md5=7f5df77e1a5fb2850708d8762797247e",,,,,,,,"Studies in Computational Intelligence",,"

Discrete-time recurrent high order neural observer for induction motors

A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability simulation results are included. � Springer-Verlag Berlin Heidelberg 2007

Discrete-time output trajectory tracking

In this chapter, two schemes for trajectory tracking based on the backstepping and the block control techniques, respectively, are proposed, using an RHONO. This observer is based on a discrete-time recurrent high-order neural network (RHONN), which estimates the state of the unknown plant dynamics. The learning algorithm for the RHONN is based on an EKF. Once the neural network structure is determined, the backstepping and the block control techniques are used to develop the corresponding trajectory tracking controllers. The respective stability analyzes, using the Lyapunov approach, for the neural observer trained with the EKF and the controllers are included. Finally, the applicability of the proposed design is illustrated by an example: output trajectory tracking for an induction motor. © 2008 Springer-Verlag Berlin Heidelberg

Discrete-time adaptive backstepping nonlinear control via high-order neural networks

This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor. � 2007 IEEE

[Histological and molecular alterations in inflammatory myopathies]

In this chapter, important mathematical preliminaries, required in future chapters, are presented. " 2008 Springer-Verlag Berlin Heidelberg.",,,,,,"10.1007/978-3-540-78289-6_2",,,"http://hdl.handle.net/20.500.12104/42692","http://www.scopus.com/inward/record.url?eid=2-s2.0-46949097250&partnerID=40&md5=aad011c37200133d75b3880bd1ce1419",,,,,,,,"Studies in Computational Intelligence",,"

Real-time output trajectory tracking using a discrete neural backstepping controller

This paper deals with adaptive tracking for discrete-time MIMO nonlinear systems in presence of bounded disturbances. A high order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). The learning algorithm for the HONN is based on an Extended Kalman Filter (EKF). This paper also includes the respective stability analysis, using the Lyapunov approach. The proposed scheme is implemented in real-time to control a three phase induction motor, as to track a time-variying speed reference and a constant flux magnitude reference. �2008 IEEE

Discrete-time modeling and control of PMSM

In this chapter, two schemes for trajectory tracking based on the backstepping and the block control techniques, respectively, are proposed, using an RHONO. This observer is based on a discrete-time recurrent high-order neural network (RHONN), which estimates the state of the unknown plant dynamics. The learning algorithm for the RHONN is based on an EKF. Once the neural network structure is determined, the backstepping and the block control techniques are used to develop the corresponding trajectory tracking controllers. The respective stability analyzes, using the Lyapunov approach, for the neural observer trained with the EKF and the controllers are included. Finally, the applicability of the proposed design is illustrated by an example: output trajectory tracking for an induction motor. " 2008 Springer-Verlag Berlin Heidelberg.",,,,,,"10.1007/978-3-540-78289-6_6",,,"http://hdl.handle.net/20.500.12104/40712","http://www.scopus.com/inward/record.url?eid=2-s2.0-46949106155&partnerID=40&md5=91bde7905c7dacfc29b2422b99a95411",,,,,,,,"Studies in Computational Intelligence",,"5

Introduction

The ultimate goal of control engineering is to implement an automatic system that could operate with increasing independence from human actions in an unstructured and uncertain environment. Such a system may be named autonomous or intelligent. It would need only to be presented with a goal and would achieve its objective by learning through continuous interaction with its environment through feedback about its behavior. One class of models that has the capability to implement this learning is the artificial neural networks. Indeed, the neural morphology of the nervous system is quite complex to analyze. Nevertheless, simplified analogies have been developed, which could be used for engineering applications. Based on these simplified understandings, artificial neural networks are built. An artificial neural network is a massively parallel distributed processor, inspired from biological neural networks, which can store experimental knowledge and makes it available for use. An artificial neural network consists of a finite number of neurons (structural element), which are interconnected to each other. It has some similarities with the brain, such as knowledge is acquired through a learning process and interneuron connectivity named as synaptic weights are used to store this knowledge, among others. © 2008 Springer-Verlag Berlin Heidelberg