Photoelectric encoder signal compensation technology based on improved mean simplified PSO model

With the development of artificial intelligence technology, photoelectric encoder signal compensation technology has become crucial in automatic control systems, while environmental interference and system noise can easily cause signal distortion, which in turn affects control accuracy. Therefore, this study proposes an improved photoelectric encoder signal compensation technology that integrates genetic algorithm and mean-simplified particle swarm optimization algorithm to achieve efficient and accurate signal compensation. Experimental results show that the peak signal-to-noise ratio of the model reaches 42.8dB, and the mean square error between the model and the real signal is 5.1%. The model converged after the 37th iteration, the convergence time was 10s, and the memory usage was 198MB. The model maintained a compensation accuracy of 95.1% and 97.4% under Gaussian and salt and pepper noise interference, respectively. The above results show that the model has high computational efficiency while ensuring the accuracy of signal compensation, and provides an efficient and stable solution for photoelectric encoder signal compensation.