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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: A recurrent wavelet-based neuro fuzzy network (RWNFN) is proposed in this paper. The proposed RWNFN integrates wavelet transforms with fuzzy rules. Temporal relations are embedded in the network by adding feedback connections from memory units in the third layer of a feedforward wavelet-based neuro fuzzy network (WNFN). The RWNFN augments the basic ability of the WNFN to overcome temporal problems. Moreover, an online learning algorithm is proposed to automatically construct the RWNFN. Computer simulations were conducted to illustrate the performance and applicability of the proposed system. [Copyright &y& Elsevier]