Paper #4

Neural Nets Distributed on Microcontrollers using Metaheuristic Parallel Optimization Algorithm

Fazal Noor and Hatem ElBoghdadi

Abstract: Metaheuristic algorithms are powerful methods for solving compute intensive problems. neural Networks, when trained well, are great at prediction and classification type of problems. Backpropagation is the most popular method utilized to obtain the weights of Neural Nets though it has some limitations of slow convergence and getting stuck in a local minimum. In order to overcome these limitations, in this paper, a hybrid method combining the parallel distributed bat algorithm with backpropagation is proposed to compute the weights of the Neural Nets. The aim is to use the hybrid method in applications of a distributed nature. Our study uses the Matlab® software and Arduino® microcontrollers as a testbed. To test the performance of the testbed, an application in the area of speech recognition is carried out. Due to the resource limitations of Arduino microcontrollers, the core speech pre-processing of LPC (linear predictive coding) feature extractions are done in Matlab® and only the LPC parameters are passed to the Neural Nets, which are implemented on Arduino microcontrollers. The experimental results show that the proposed scheme does produce promising results.

Keywords: Bat algorithm; Genetic algorithm; Neural Networks; Speech recognition; Optimization algorithm.