Markov random field modeling in computer vision
Markov random field modeling in computer vision
Selected papers from the 2nd Scottish Functional Programming Workshop (SFP00)
Constrained least squares design of 2-D FIR filters
IEEE Transactions on Signal Processing
Three different criteria for the design of two-dimensional zerophase FIR digital filters
IEEE Transactions on Signal Processing
A weighted least squares algorithm for quasi-equiripple FIR and IIRdigital filter design
IEEE Transactions on Signal Processing
Expectation maximization enhancement with evolutionstrategy for stochastic ontology mapping
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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In this paper the design of maximally flat linear phase finite impulse response (FIR) filters is considered. The problem with using the genetic algorithm (GA) in this kind of problems is the high cost of evaluating the fitness for each string in the population. The designing of optimum FIR filters under given constraints and required criteria includes exhaustive number of evaluations for filter coefficients, and the repetitive evaluations of objective functions that implicitly constitutes construction of the filter transfer functions. This problem is handled here with acceptable results utilizing Markov random fields (MRF's) approach. We establish a new theoretical approach here and we apply it on the design of FIR filters. This approach allows us to construct an explicit probabilistic model of the GA fitness function forming what is called the ''Ising GA'' that is based on sampling from a Gibbs distribution. Ising GA avoids the exhaustive design of suggested FIR filters (solutions) for every string of coefficients in every generation and replace this by a probabilistic model of fitness every gap (period) of iterations. Experimentations done with Ising GA of probabilistic fitness models are less costly than those done with standard GA and with high quality solutions.