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Abstract: This paper introduces implementation of a wavelet neural network (WNN) with learning ability on field programmable gate array (FPGA). A learning algorithm using gradient descent method is not easy to implement in an electronic circuit and has local minimum. A more suitable method is the particle swarm optimization (PSO) that is a population-based optimization algorithm. The PSO is similar to the GA, but it has no evolution operators such as crossover and mutation. In the approximation of a nonlinear activation function, we use a Taylor series and a look-up table (LUT) to achieve a more accurate approximation. The results of the two experiments demonstrate the successful hardware implementation of the wavelet neural networks with the PSO algorithm using FPGA. From the results of the experiment, it can be seen that the performance of the PSO is better than that of the simultaneous perturbation algorithm at sufficient particle sizes. [Copyright &y& Elsevier]

Abstract: We study the gluonic sector of the three-flavor PNJL model by obtaining the adjoint Polyakov loop and the gluon distribution function in the mean-field approximation. Besides, we explore the thermal strange quark pair-production processes, and , with the aid of the three-flavor PNJL model. The results help us identify the temperature where the gluonic contribution to the production rate becomes dominant, which is an innovative phenomenon compared with the result obtained in free perturbation theory. [Copyright &y& Elsevier]