136,175 research outputs found
Analysis and synthesis of weighted-sum functions
A weighted-sum (WS) function computes the sum of selected integers. This paper considers a design method for WS functions by look-up table (LUT) cascades. In particular, it derives upper bounds on the column multiplicities of decomposition charts for WS functions. From these, the size of LUT cascades that realize WS functions can be estimated. The arithmetic decomposition of a WS function is also shown. With this method, a WS function can be implemented with cascades and adders
Comparison of Weighted Sum Fitness Functions for PSO Optimization of Wideband Medium-gain Antennas
In recent years PSO (Particle Swarm Optimization) has been successfully applied in antenna design. It is well-known that the cost function has to be carefully chosen in accordance with the requirements in order to reach an optimal result. In this paper, two different wideband medium-gain arrays are chosen as benchmark structures to test the performance of four PSO fitness functions that can be considered in such a design. The first one is a planar 3 element, the second one a linear 4 element antenna. A MoM (Method of Moments) solver is used in the design. The results clearly show that the fitness functions achieve a similar global best candidate structure. The fitness function based on realized gain however converges slightly faster than the others
Non-Zero Sum Games for Reactive Synthesis
In this invited contribution, we summarize new solution concepts useful for
the synthesis of reactive systems that we have introduced in several recent
publications. These solution concepts are developed in the context of non-zero
sum games played on graphs. They are part of the contributions obtained in the
inVEST project funded by the European Research Council.Comment: LATA'16 invited pape
Gradient and Passive Circuit Structure in a Class of Non-linear Dynamics on a Graph
We consider a class of non-linear dynamics on a graph that contains and
generalizes various models from network systems and control and study
convergence to uniform agreement states using gradient methods. In particular,
under the assumption of detailed balance, we provide a method to formulate the
governing ODE system in gradient descent form of sum-separable energy
functions, which thus represent a class of Lyapunov functions; this class
coincides with Csisz\'{a}r's information divergences. Our approach bases on a
transformation of the original problem to a mass-preserving transport problem
and it reflects a little-noticed general structure result for passive network
synthesis obtained by B.D.O. Anderson and P.J. Moylan in 1975. The proposed
gradient formulation extends known gradient results in dynamical systems
obtained recently by M. Erbar and J. Maas in the context of porous medium
equations. Furthermore, we exhibit a novel relationship between inhomogeneous
Markov chains and passive non-linear circuits through gradient systems, and
show that passivity of resistor elements is equivalent to strict convexity of
sum-separable stored energy. Eventually, we discuss our results at the
intersection of Markov chains and network systems under sinusoidal coupling
Synthesis of neural networks for spatio-temporal spike pattern recognition and processing
The advent of large scale neural computational platforms has highlighted the
lack of algorithms for synthesis of neural structures to perform predefined
cognitive tasks. The Neural Engineering Framework offers one such synthesis,
but it is most effective for a spike rate representation of neural information,
and it requires a large number of neurons to implement simple functions. We
describe a neural network synthesis method that generates synaptic connectivity
for neurons which process time-encoded neural signals, and which makes very
sparse use of neurons. The method allows the user to specify, arbitrarily,
neuronal characteristics such as axonal and dendritic delays, and synaptic
transfer functions, and then solves for the optimal input-output relationship
using computed dendritic weights. The method may be used for batch or online
learning and has an extremely fast optimization process. We demonstrate its use
in generating a network to recognize speech which is sparsely encoded as spike
times.Comment: In submission to Frontiers in Neuromorphic Engineerin
Lipschitz Robustness of Finite-state Transducers
We investigate the problem of checking if a finite-state transducer is robust
to uncertainty in its input. Our notion of robustness is based on the analytic
notion of Lipschitz continuity --- a transducer is K-(Lipschitz) robust if the
perturbation in its output is at most K times the perturbation in its input. We
quantify input and output perturbation using similarity functions. We show that
K-robustness is undecidable even for deterministic transducers. We identify a
class of functional transducers, which admits a polynomial time
automata-theoretic decision procedure for K-robustness. This class includes
Mealy machines and functional letter-to-letter transducers. We also study
K-robustness of nondeterministic transducers. Since a nondeterministic
transducer generates a set of output words for each input word, we quantify
output perturbation using set-similarity functions. We show that K-robustness
of nondeterministic transducers is undecidable, even for letter-to-letter
transducers. We identify a class of set-similarity functions which admit
decidable K-robustness of letter-to-letter transducers.Comment: In FSTTCS 201
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