Designing and comparing multiple portfolios of parameter configurations for online algorithm selection

National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ

On the stability of fuzzy systems

Abstract — This paper studies the global asymptotic stability of a class of fuzzy systems. It demonstrates the equivalence of stability properties of fuzzy systems and linear time invariant (LTI) switching systems. A necessary condition and a sufficient condition for the stability of such systems are given, and it is shown that under the sufficient condition, a common Lyapunov function exists for the LTI subsystems. A particular case when the system matrices can be simultaneously transformed to normal matrices is shown to correspond to the existence of a common quadratic Lyapunov function. A constructive procedure to check the possibility of simultaneous transformation to normal matrices is provided. Index Terms—Asymptotic stability, switching systems. I

Absolute expediency of Q-and S-model learning algorithms

A class of nonlinear learning algorithms for the Q-and S-model stochastic automaton-random environment setup are described. Necessary and sufficient conditions for absolute expediency of these algorithms are derived. Various algorithms that are so far reported in literature can be obtained as special cases of the general algorithm given in this correspondence

A generalised stability multiplier

A generalised stability multiplier has been developed for the asymptotic stability in the large of a feedback system containing a power-law non-linearity. The multiplier is of the form Z<SUB>T</SUB>(jω)=1+αjω+β[Y<SUB>1</SUB>(jω)+Y<SUB>2</SUB>(jω)]+(1-α)[Y(jω)-Y(-jω)] and the multipliers developed in the earlier literature have been shown to be special cases of this multiplier

Time-varying system stability-interchangeability of the bounds on the logarithmic variation of gain

A frequency-domain criterion for the L2-stability of systems containing a single time-varying gain in an otherwise time-invariant linear feedback loop is given. This is an improvement upon the earlier criteria presented by the authors in permitting an interchangeability of the allowable bounds on the logarithmic variation of the gain

Asymptotic behaviour of a learning algorithm

The paper considers a learning automaton operating in a stationary random environment. The automaton has multiple actions and updates its action probability vector according to the linear reward-ε penalty (LR-εp) algorithm. Using weak convergence concepts it is shown that for large time and small values of parameters in the algorithm, the evolution of the action probability can be represented by Gauss-Markov diffusion

Extended time-frequency conditions for the instability of a class of non-linear time-varying systems

This paper presents new criteria for the L 2-input, L2-output stability of a class of feedback systems containing a time-invariant convolution operator and a time varying non-linearity in cascade, in a negative feedback loop. These are sufficient conditions for the system stability derived using the multiplier concept and involve certain time-domain conditions on the multiplier. The method of derivation draws on the theory of positivity of compositions of operators and time-varying gains and necessitates an upper bound on the rate of variation of the time-varying gain