Gray Neural Network Algorithm with 11 Input Variables for FOA-GM Integration

The Gray Neural Network Algorithm with 11 input variables, designed for integration with FOA-GM (likely referring to Fruit Fly Optimization Algorithm-Gray Model variant), represents a hybrid prediction model that combines gray system theory with neural networks. The core methodology involves using gray models to handle data uncertainty while leveraging neural networks' powerful nonlinear fitting capabilities to enhance prediction accuracy.

The algorithm's typical architecture operates in three stages: Gray Preprocessing: Performs gray relational analysis or GM(1,N) modeling on 11-dimensional input data to extract gray relational characteristics and reduce noise in original data. This can be implemented through gray incidence degree calculation functions that quantify relationships between system factors. Neural Network Mapping: Uses gray-processed features as input to neural networks (such as BP networks), where hidden layers learn complex nonlinear relationships between inputs and outputs. Implementation typically involves defining network architecture with 11 input neurons, hidden layers with activation functions like sigmoid/tanh, and appropriate output layer configuration. FOA-GM Optimization: Likely employs Fruit Fly Optimization Algorithm (FOA) or other optimization methods to adjust gray module parameters or neural network weights, preventing local optimum issues. The FOA implementation would include population initialization, smell-based search operations, and iterative fitness evaluation for parameter optimization.

Key advantages include: Suitability for small-sample, high-noise scenarios with 11-dimensional inputs Gray theory compensating for neural networks' weak interpretability of data uncertainty Balanced computational efficiency through modular design for multiple inputs

Typical applications encompass complex industrial parameter prediction, multi-factor economic indicator analysis, and other domains requiring processing of high-dimensional uncertain data.