Cellular Neural Network (CNN) Parallel Computing Model

The Cellular Neural Network (CNN) represents a parallel computing architecture that mirrors biological neural networks through specialized communication channels between adjacent processing units. This model finds extensive applications in image processing, pattern recognition, and natural language processing domains. The implementation employs two fundamental matrices: Matrix A defines the feedback template governing state retention and recurrence, while Matrix B specifies the control template managing input signal propagation. The operational principle involves iterative training processes where these matrices undergo optimization through learning algorithms to enhance prediction accuracy and classification performance. Key functions typically include neighborhood state aggregation using convolution operations, nonlinear activation functions (often implemented through sigmoid or tanh transformations), and parallel state updates across all cellular units. Performance optimization is achieved by fine-tuning template matrices through gradient-based learning methods or genetic algorithms, improving feature extraction capabilities and processing efficiency for complex data patterns.