Digit Recognition Method Based on Discrete Hopfield Neural Network Optimized by Artificial Fish Swarm Algorithm

Abstract

To solve the problems that the weights and thresholds of discrete Hopfield neural networks are easy to fall into local optima and have insufficient anti-noise ability in digit recognition, a digit recognition method based on discrete Hopfield neural network is proposed, which is optimized by fish swarm algorithm and called AFSA-HOP integration method. The parameters of the discrete Hopfield neural network are optimized by using AFSA's powerful global search ability, and the recognition accuracy of the Hopfield neural network is taken as the fitness function. This allows the Hopfield neural network to maintain a high associative success rate even under high noise-to-signal ratios. Computer simulation experiments show that while the recognition performance of the traditional Hopfield neural network significantly deteriorates when the noise intensity is 0.2, the AFSA-HOP method maintains a high recognition accuracy even at noise intensities of 0.4 and 0.5, demonstrating superior digital recognition performance. This method provides a robust new approach for digital recognition and could be further extended in future applications by integrating other optimization algorithms.