Search results for "learning theory"
showing 10 items of 216 documents
On the Calmness of a Class of Multifunctions
2002
The paper deals with the calmness of a class of multifunctions in finite dimensions. Its first part is devoted to various conditions for calmness, which are derived in terms of coderivatives and subdifferentials. The second part demonstrates the importance of calmness in several areas of nonsmooth analysis. In particular, we focus on nonsmooth calculus and solution stability in mathematical programming and in equilibrium problems. The derived conditions find a number of applications there.
Stability of genetic regulatory networks with time-varying delay: Delta operator method
2015
This paper investigates the stability problem for a class of uncertain genetic regulatory networks (GRNs) with time-varying delay via delta operator approach. Both the parameter uncertainty and the generalized activations are considered in the model under study. By constructing an appropriate Lyapunov-Krasovskii functional, the stability and robust stability conditions of GRNs are presented under the delta operator frame. These conditions can be expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is employed to illustrate the effectiveness of the proposed results.
Adaptive and Generative Learning: Implications from Complexity Theories
2008
One of the most important classical typologies within the organizational learning literature is the distinction between adaptive and generative learning. However, the processes of these types of learning, particularly the latter, have not been widely analyzed and incorporated into the organizational learning process. This paper puts forward a new understanding of adaptive and generative learning within organizations, grounded in some ideas from complexity theories: mainly self-organization and implicate order. Adaptive learning involves any improvement or development of the explicate order through a process of self-organization. Self-organization is a self-referential process characterized …
Education Students’ Use of Collaborative Writing Tools in Collectively Reflective Essay Papers
2014
Published version of an article in the journal: Journal of Information Technology Education: Research. Also available from the publisher at: http://www.jite.org/documents/Vol13/JITEv13ResearchP091-120Brodahl0463.pdf Open Access Google Docs and EtherPad are Web 2.0 tools providing opportunity for multiple users to work online on the same document consecutively or simultaneously. Over the last few years a number of research papers on the use of these collaborative tools in a teaching and learning environment have been published. This work builds on that of Brodahl, Hadjerrouit, and Hansen (2011) expanding its case study. The theoretical framework is the same as the one underlying Brodahl et a…
On the inductive inference of recursive real-valued functions
1999
AbstractWe combine traditional studies of inductive inference and classical continuous mathematics to produce a study of learning real-valued functions. We consider two possible ways to model the learning by example of functions with domain and range the real numbers. The first approach considers functions as represented by computable analytic functions. The second considers arbitrary computable functions of recursive real numbers. In each case we find natural examples of learnable classes of functions and unlearnable classes of functions.
Learning formulae from elementary facts
1997
Since the seminal paper by E.M. Gold [Gol67] the computational learning theory community has been presuming that the main problem in the learning theory on the recursion-theoretical level is to restore a grammar from samples of language or a program from its sample computations. However scientists in physics and biology have become accustomed to looking for interesting assertions rather than for a universal theory explaining everything.
On the stability of spline-collocation methods of multivalue type
1987
In this paper the general classV of spline-collocation methods for first order systems of ordinary differential equations is investigated. The methods can in part be regarded as so-called multivalue methods. This type contains the generalized singly-implicit methods treated by Butcher.
Online Pricing via Stackelberg and Incentive Games in a Micro-Grid
2019
This paper deals with the analysis and design of online pricing mechanisms in micro-grids. Two cases are studied in which the market layer is modeled as an open-loop and closed-loop dynamical system respectively. In the case of open-loop market dynamics, the price is generated as equilibrium price of a Stackelberg game with an incentive strategy. In such Stackelberg game, the leader is the energy supplier, the follower is the consumer, and the leader plays an incentive strategy. In the case of closed-loop market dynamics, the price is obtained as a function of the power supplied and the demand. A stability analysis is provided for both cases, which sheds light on the transient and steady-st…
Crowd-Averse Cyber-Physical Systems: The Paradigm of Robust Mean-Field Games
2016
For a networked controlled system, we illustrate the paradigm of robust mean-field games. This is a modeling framework at the interface of differential game theory, mathematical physics, and $H_{\infty}$ - optimal control that tries to capture the mutual influence between a crowd and its individuals. First, we establish a mean-field system for such games including the effects of adversarial disturbances. Second, we identify the optimal response of the individuals for a given population behavior. Third, we provide an analysis of equilibria and their stability.
Organized Learning Models (Pursuer Control Optimisation)
1982
Abstract The concept of Organized Learning is defined, and some random models are presented. For Not Transferable Learning, it is necessary to start from an instantaneous learning; by a discrete way, we must form a stochastic model considering the probability of each path; with a continue aproximation, we can study the evolution of the internal state through to consider the relative and absolute probabilities, by means of differential equations systems. For Transferable Learning, the instantaneous learning give us directly the System evolution. So, the Algoritmes for the different models are compared.