Vol 52, No 2 (2018)

The problem of production rules extraction is discussed. The computational complexity of the method for production rules extraction on the basis of parallel computing and computational intelligence is analyzed. Theoretical estimations of the speedup and efficiency of the method are found. Software implementing of the method in С++ with using the MPI library and providing the production rules extraction of the given observation sets is developed. Experiments for practical tasks are carried out.

In this paper, examining some games, we show that classical techniques are not always effective for games with not many stages and players and it can’t be claimed that these techniques of solution always obtain the optimal and actual Nash equilibrium point. For solving these problems, two evolutionary algorithms are then presented based on the population to solve general dynamic games. The first algorithm is based on the genetic algorithm and we use genetic algorithms to model the players' learning process in several models and evaluate them in terms of their convergence to the Nash Equilibrium. in the second algorithm, a Particle Swarm Intelligence Optimization (PSO) technique is presented to accelerate solutions’ convergence. It is claimed that both techniques can find the actual Nash equilibrium point of the game keeping the problem’s generality and without imposing any limitation on it and without being caught by the local Nash equilibrium point. The results clearly show the benefits of the proposed approach in terms of both the quality of solutions and efficiency.

The features of infrared polarization and intensity images are not finely transferred to the fused image by using traditional fusion algorithms, which leads to a severe blur of the fused image. This study proposes a new infrared polarization and intensity image fusion algorithm based on the feature transfer. First, the contrast features of the infrared polarization image are extracted by the multiscale average filter decomposition with help of standard deviation constraint. The texture features of infrared polarization images are retrieved via non-subsample-shearlet transform at the same time. Second, the difference of the features is measured using the similarity index, which is used as the transfer weight for the infrared polarization feature images during the later phase of the image fusion. Finally, the fused image is obtained by the superimposition of the infrared intensity image and feature images, which are created from the infrared polarization image. The experimental results demonstrated that the proposed method is able to transfer the features of both the infrared intensity image and the polarization image into the fused images. It performs well on both subjective and objective image quality.

Block compressed sensing (BCS) has great potential in image compression applications for its low storage requirement and low computational complexity. However, the sampling efficiency of traditional BCS is very poor since some blocks actually are not sparse enough to apply compressed sensing (CS). In order to improve the sampling efficiency, a novel BCS with random permutation and reweighted sampling (BCS-RP-RS) for image compression applications is proposed. In the proposed method, two effective strategies, including random permutation and reweighted sampling, are used simultaneously to guarantee all blocks of image signals sparse enough to apply CS. As a result, better sampling efficiency can be achieved. Simulation results show that the proposed approach improves the peak signal-to-noise ratio (PSNR) of the reconstructed-images significantly compared with the conventional BCS with random permutation (BCS-RP) approach.