FPGA Implementation of ANN Training using Levenberg and Marquardt Algorithm

Mehmet Ali Çavuşlu, Suhap Şahin


ANN training using gradient based LM algorithm has been implemented on FPGA for the solution of dynamic system identification problems within the scope of study. In the implementation, IEEE 754 floating-point number format has been used because of dynamism and sensitivity that it has provided. Mathematical approaches have been preferred in order to implement the activation function, which is the most critical phase of the study. ANN is tested by using input-output sample sets, which are shown or not shown to the network in training phase, and success rates are given for every sample set. The results obtained show that it is possible to iplementation of ANN training as FPGA based by using LM algorithm and that the ANN make a good generalization.


Levenberg and Marquardt; FPGA; MLP; ANN training

DOI: http://dx.doi.org/10.14311/NNW.2018.%25x


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