This work is concerned with the problem of experiment design for the efficient identification of a linear single input, single output dynamic system from input-output data in the presence of disturbances. The experimenter is allowed to select certain factors under his control (input signal, output filter, sampling times), subject to suitable constraints, in order to maximise information from an experiment. A frequency domain approach to the test signal/sampling rate design problem is adopted and the cost criterion is chosen to be a suitable convex scalar function of the inverse Fisher information matrix. A geometrical approach to the design problem is developed for beth continuous-tlme and discrete-time systems, based on the theory of Tchebycheff systems and their associated moment spaces. Conditions are derived for the existence of certain minimal representations of the optimal input spectrum, leading to a reduction in the dimension of the design optimization problem. In particular, for a restricted class of model structures, the optima/ input spectrum contains the minimum number of frequencies consistent with a persistently exciting signal. A class of sequential design algorithms is proposed with proven global convergence to a D-optlmal design. Comparison is made of the computational efficiency of a number of these algorithms.

Lecture notes in control and information sciences

A reinforcement scheme that is based on the linear reward-inaction updating algorithm is presented for a learning automaton whose action set changes from instant to instant. A learning automaton using the algorithm is shown to be both absolutely expedient and e-optimal. The simulation results verify the e-optimality of the algorithm. The results can be extended to the design of general nonlinear absolutely expedient learning algorithms.

Learning automata with changing number of actions

This series aims to report new developments in the fields of control and information sciences quickly, informally and at a high level. The type of material considered for publication includes: 1. Preliminary drafts of monographs and advanced textbooks 2. Lectures on a new field, or presenting a new angle on a classical field 3. Research reports 4. Reports of meetings, provided they are a) of exceptional interest and b) devoted to a specific topic. The timeliness of subject material is very important.  Indexed by EI-Compendex, SCOPUS, Ulrich ́s, MathSciNet, Current Index to Statistics, Current Mathematical Publications, Mathematical Reviews, IngentaConnect, MetaPress and Springerlink.

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Lecture Notes in Control and Information Sciences

A learning automaton is a machine that interacts with a random environment and that simultaneously learns the optimal action that the environment offers to it Learning automata with variable structure are considered Such automata are completely defined by a set of probability updating rules Contrary to all the variable-structure stochastic automata (VSSA) discussed in the literature, which update the probabilities in such a way that an action probability can take any real value in the interval [0,1], the probability space is discretized so as to permit the action probability to assume one of a finite number of distinct values in [0,1] The discretized automaton is termed linear or nonlinear depending on whether the subintervals of [0,1] are of equal length It is proven that 1) discretized two-action linear reward-inaction automata are absorbing and ?-optimal in all environments; 2) discretized two-action linear inaction-penalty automata are ergodic and expedient in all environments; 3) discretized two-action linear inaction-penalty learning automata with artificially created absorbing barriers are ?-optimal in all random environments; and 4) there exist nonlinear discretized reward-inaction automata that are ?-optimal in all random environments The maximum advantage gained by rendering any finite-state discretized automaton nonlinear has also been derived

Absorbing and Ergodic Discretized Two-Action Learning Automata

In this paper, we first introduce a network of learning automata, which we call it as distributed learning automata and then propose some iterative algorithms for solving stochastic shortest path problem These algorithms use distributed learning automata to find a policy that determines a path from a source node to a destination node with minimal expected cost (length) In these algorithms, at each stage distributed learning automata determines which edges to be sampled This sampling method may result in decreasing unnecessary samples and hence decreasing the running time of algorithms It is shown that the shortest path is found with a probability as close as to unity by proper choice of the parameters of the proposed algorithms

Utilizing distributed learning automata to solve stochastic shortest path problems

Absolutely Expedient Learning Algorithms For Stochastic Automata

Bounds on the Convergence Probabilities of Learning Automata

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Absolutely Expedient Learning Algorithms For Stochastic Automata

Bounds on the Convergence Probabilities of Learning Automata