5,570 research outputs found
Small gain theorems for large scale systems and construction of ISS Lyapunov functions
We consider interconnections of n nonlinear subsystems in the input-to-state
stability (ISS) framework. For each subsystem an ISS Lyapunov function is given
that treats the other subsystems as independent inputs. A gain matrix is used
to encode the mutual dependencies of the systems in the network. Under a small
gain assumption on the monotone operator induced by the gain matrix, a locally
Lipschitz continuous ISS Lyapunov function is obtained constructively for the
entire network by appropriately scaling the individual Lyapunov functions for
the subsystems. The results are obtained in a general formulation of ISS, the
cases of summation, maximization and separation with respect to external gains
are obtained as corollaries.Comment: provisionally accepted by SIAM Journal on Control and Optimizatio
Numerical Implementation of Gradient Algorithms
A numerical method for computational implementation of gradient dynamical systems is presented. The method is based upon the development of geometric integration numerical methods, which aim at preserving the dynamical properties of the original ordinary differential
equation under discretization. In particular, the proposed method belongs to the class of discrete gradients methods, which substitute the gradient of the continuous equation with a discrete gradient, leading to a map that possesses the same Lyapunov function of the dynamical system,
thus preserving the qualitative properties regardless of the step size. In this work, we apply a discrete gradient method to the implementation of Hopfield neural networks. Contrary to most geometric integration
methods, the proposed algorithm can be rewritten in explicit form, which considerably improves its performance and stability. Simulation results show that the preservation of the Lyapunov function leads to an improved performance, compared to the conventional discretization.Spanish Government project no. TIN2010-16556 Junta de AndalucÃa project no. P08-TIC-04026 Agencia Española de Cooperación Internacional
para el Desarrollo project no. A2/038418/1
Path Integral Approach to Random Neural Networks
In this work we study of the dynamics of large size random neural networks.
Different methods have been developed to analyse their behavior, most of them
rely on heuristic methods based on Gaussian assumptions regarding the
fluctuations in the limit of infinite sizes. These approaches, however, do not
justify the underlying assumptions systematically. Furthermore, they are
incapable of deriving in general the stability of the derived mean field
equations, and they are not amenable to analysis of finite size corrections.
Here we present a systematic method based on Path Integrals which overcomes
these limitations. We apply the method to a large non-linear rate based neural
network with random asymmetric connectivity matrix. We derive the Dynamic Mean
Field (DMF) equations for the system, and derive the Lyapunov exponent of the
system. Although the main results are well known, here for the first time, we
calculate the spectrum of fluctuations around the mean field equations from
which we derive the general stability conditions for the DMF states. The
methods presented here, can be applied to neural networks with more complex
dynamics and architectures. In addition, the theory can be used to compute
systematic finite size corrections to the mean field equations.Comment: 20 pages, 5 figure
Discrete Dynamical Systems Embedded in Cantor Sets
While the notion of chaos is well established for dynamical systems on
manifolds, it is not so for dynamical systems over discrete spaces with
variables, as binary neural networks and cellular automata. The main difficulty
is the choice of a suitable topology to study the limit . By
embedding the discrete phase space into a Cantor set we provided a natural
setting to define topological entropy and Lyapunov exponents through the
concept of error-profile. We made explicit calculations both numerical and
analytic for well known discrete dynamical models.Comment: 36 pages, 13 figures: minor text amendments in places, time running
top to bottom in figures, to appear in J. Math. Phy
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