Gradient-like dynamics in neural networks

This report presents a formalism that enables the dynamics of a broad class of neural networks to be understood. A number of previous works have analyzed the Lyapunov stability of neural network models. This type of analysis shows that the excursion of the solutions from a stable point is bounded. The purpose of this work is to present a model of the dynamics that also describes the phase space behavior as well as the structural stability of the system. This is achieved by writing the general equations of the neural network dynamics as a gradient-like system. In this paper some important properties of gradient-like systems are developed and then it is demonstrated that a broad class of neural network models are expressible in this form

On the control of a high power backward-wave oscillator using quantifier elimination methods

This paper presents an experimental/theoretical study of methods to identify and control a repetitively-pulsed high power microwave source. A neural network was used to model the system and quantifier elimination (QE) theory is used to search for suitable operating conditions

Gradient and Hamiltonian dynamics applied to learning in neural networks

The process of machine learning can be considered in two stages model selection and parameter estimation. In this paper a technique is presented for constructing dynamical systems with desired qualitative properties. The approach is based on the fact that an n-dimensional nonlinear dynamical system can be decomposed into one gradient and (n-1) Hamiltonian systems. Thus, the model selection stage consists of choosing the gradient and Hamiltonian portions appropriately so that a certain behavior is obtainable. To estimate the parameters, a stably convergent learning rule is presented. This algorithm has been proven to converge to the desired system trajectory for all initial conditions and system inputs. This technique can be used to design neural network models which are guaranteed to solve the trajectory learning problem

An application of gradient-like dynamics to neural networks

This paper reviews a formalism that enables the dynamics of a broad class of neural networks to be understood. This formalism is then applied to a specific network and the predicted and simulated behavior of the system are compared. The purpose of this work is to utilise a model of the dynamics that also describes the phase space behavior and structural stability of the system. This is achieved by writing the general equations of the neural network dynamics as a gradient-like system. In this paper it is demonstrated that a network with additive activation dynamics and Hebbian weight update dynamics can be expressed as a gradient-like system. An example of an S-layer network with feedback between adjacent layers is presented. It is shown that the process of weight learning is stable in this network when the learned weights are symmetric. Furthermore, the weight learning process is stable when the learned weights are asymmetric, provided that the activation is computed using only the symmetric part of the weights

General Neural Networks Dynamics are a Superposition of Gradient-like and Hamiltonian-like Systems

This report presents a formalism that enables the dynamics of a broad class of neural networks to be understood. A number of previous works have analyzed the Lyapunov stability of neural network models. This type of analysis shows that the excursion of the solutions from a stable point is bounded. The purpose of this work is to present a model of the dynamics that also describes the phase space behavior as well as the structural stability of the system. This is achieved by writing the general equations of the neural network dynamics as the sum of gradient-like and Hamiltonian-like systems. In this paper some important properties of both gradient-like and Hamiltonian-like systems are developed and then it is demonstrated that a broad class of neural network models are expressible in this form

Optimal discrete-time control for non-linear cascade systems

In this paper we develop an optimality-based framework for designing controllers for discrete-time nonlinear cascade systems. Specifically, using a nonlinear-nonquadratic optimal control framework we develop a family of globally stabilizing backstepping-type controllers parameterized by the cost functional that is minimized. Furthermore, it is shown that the control Lyapunov function guaranteeing closed-loop stability is a solution to the steady-state Bellman equation for the controlled system and thus guarantees both optimality and stability

LQ Robust Synthesis With Non-fragile Controllers: The Static State Feedback Case

This paper describes the synthesis of Non-fragile or Resilient regulators for linear systems. The general framework for fragility is described using state space methodologies and the LQH static state feedback case is examined in detail. We discuss the multiplicative structured uncertainties case and propose remedies of the fragility problem. The benchmark problem is taken as example to show how an uncertain or resilient static state feedback controller can affect the performance of the system

Bilateral Teleoperation of Mobile Robot over Delayed Communication Network: Implementation

In a previous paper we proposed a bilateral teleoperation framework of a wheeled mobile robot over communication channel with constant time delay. In this paper we present experimental results. Our goal is to illustrate and validate the properties of the proposed scheme as well as to present practical implementation issues and the adopted solutions. In particular, the bilaterally teleoperated system is passive and the system is stable in the presence of time delay. Internet has been used as the communication channel and a buffer has been implemented to maintain a constant time delay and to handle packet order

Solid flux in travelling fluidized bed operating in square-nosed slugging regime

The performance of gas-fluidized bed reactors depends significantly on their hydrodynamics. Among the important properties that dictate the characteristics of a gas-fluidized bed, local solid flux plays a significant role, influencing vital parameters such as bed-to-surface heat exchange and solid circulation rate. Developing techniques that can provide accurate measurements of solid flux is extremely important for: 1) assessing the accuracy of other measurement techniques applicable to industrial units, and 2) validation of CFD models. Comparison of different measurement techniques that provide similar hydrodynamic information is helpful in assessing the errors associated with each methodology. Most measurement techniques for obtaining solid flux in gas-fluidized beds are based on intrusive probes that can simultaneously measure solid velocity and voidage. Previously (1), the novel travelling fluidized bed (TFB) was operated to determine particle velocity from radioactive particle tracking (RPT), positron emission particle tracking (PEPT) and borescopy with silica sand particles of mean diameter 292 μm at superficial gas velocities from 0.4 to 0.6 m/s. In this study, the TFB, operated under identical conditions, was deployed to compare RPT and PEPT for the investigation of solid flux in square-nosed slugging. Both techniques provided solid flux data of the same order, but there were significant quantitative differences. Differing physical properties of tracer particles and the bed material, and differences in the tracer localization techniques are among the factors that contributed to the observed discrepancies. The results provide useful insights on the merits and challenges associated with advanced techniques for measuring solids flux in gas-fluidized beds. REFERENCES S. Tebianian, K. Dubrawski, N. Ellis, R. A. Cocco, R. Hays, S.B.R. Karri, T. W. Leadbeater, D.J. Parker, J. Chaouki, R. Jafari, P. Garcia-Trinanes, J.P.K. Seville, J.R. Grace. Comparison of Particle Velocity Measurement Techniques in a Fluidized Bed Operating in the Square-Nosed Slugging Flow Regime. Powder Technol., 2015. doi:http://dx.doi.org/10.1016/j.powtec.2015.08.040

Human papillomavirus infection in honduran women with normal cytology

Contains fulltext : 80440.pdf (publisher's version ) (Closed access)OBJECTIVE: This study was aimed at estimating type-specific HPV prevalence and its cofactors among Honduran women with normal cytology in order to provide valuable information to health policymakers about the epidemiology of this important sexually transmitted infection. METHODS: A total of 591 women with normal cytology from Tegucigalpa, Honduras were interviewed and tested for HPV using the SPF10 LiPA25. A structured epidemiological questionnaire was administered to each woman. RESULTS: The overall HPV prevalence was 51%. Twenty-three types of HPV were detected; HPV 16, 51, 31, 18, and 11 were the most common. The highest prevalence of cancer associated HPV types (15.0%) was found in the women less than 35 years. Besides the association with age, the main independent predictors of HPV infection were the lifetime number of sexual partners and having a low socioeconomic status and less than 5 previous Pap smears. CONCLUSIONS: In the population studied, there was a broad diversity of HPV infections, with high-risk types being the most common types detected. The establishment of a well-characterized population with regard to the community prevalence of type-specific HPV infection will provide a valuable baseline for monitoring population effectiveness of an HPV vaccine