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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]

This paper presents a wavelet-based recurrent fuzzy neural network (WRFNN) for prediction and identification of nonlinear dynamic systems. The proposed WRFNN model combines the traditional Thkagi-Sugeno-Kang (TSK) fuzzy model and the wavelet neural networks (WNN). This paper adopts the nonorthogonal and compactly supported functions as wavelet neural network bases. Temporal relations embedded in the network are caused by adding some feedback connections representing the memory units into the second layer of the feedforward wavelet-based fuzzy neural networks (WFNN). An online learning algorithm, which consists of structure learning and parameter learning, is also presented. The structure learning depends on the degree measure to obtain the number of fuzzy rules and wavelet functions. Meanwhile, the parameter learning is based on the gradient descent method for adjusting the shape of the membership function and the connection weights of WNN. Finally, computer simulations have demonstrated that the proposed WRFNN model requires fewer adjustable parameters and obtains a smaller rms error than other methods. [ABSTRACT FROM AUTHOR]