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Abstract: The implementation of adaptive neural fuzzy networks (NFNs) using field programmable gate arrays (FPGA) is proposed in this study. Hardware implementation of NFNs with learning ability is very difficult. The backpropagation (BP) method in the learning algorithm is widely used in NFNs, making it difficult to implement NFNs in hardware because calculating the backpropagation error of all parameters in a system is very complex. However, we use the simultaneous perturbation method as a learning scheme for the NFN hardware implementation. In order to reduce the chip area, we utilize the traditional non-linear activation function to implement the Gaussian function. We can confirm the reasonableness of NFN performance through some examples. [Copyright &y& Elsevier]

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]