A multiplier-less GA optimized pulsed neural network for satellite image analysis using a FPGA

This paper presents a digital hardware oriented system that uses a genetic algorithm (GA) for optimizing a pattern classifier based on the pulsed neural network (PNN). The scheme avoids the usage of multipliers and dividers, which are the bottlenecks for digital hardware implementation of parallel computations like GA and neural networks. Utilizing the nature of RBF being inherent in the pulsed neural network, the scheme yields very compact computational circuits for implementation on a FPGA chip with massive parallelism that guarantees the speed of the neural and evolutionary computations. The on-chip GA-PNN system is developed for terrain classification of a multi-spectral satellite image. Experimental results show that the performance of the proposed system is comparable to a back propagation (BP) neural network while its training speed exceeds the BP network overwhelmingly.