Approximate Logic Dendritic Neuron Model Classification Based on Improved DE Algorithm

Classification is a core task in pattern recognition, with broad applications in image processing, speech recognition, and medical diagnostics. Traditional neural networks often suffer from difficulties in parameter optimization and local optima, while the performance of approximate logical dendritic neuron models depends heavily on proper parameter configuration. To address these issues, this paper proposes a Feature-Guided Adaptive Differential Evolution algorithm, which incorporates feature-guided mechanisms, adaptive parameter control, and multi-strategy mutation to enhance the standard differential evolution framework. The algorithm is applied to optimize the dendritic neuron model’s parameters, providing a more effective and robust solution for intelligent classification in complex data environments. This approach contributes to the development of reliable optimization strategies in intelligent systems and has promising application potential.