Implementing BP Neural Network-Based Data Aggregation for Enhanced Financial Oversight in Biotechnological Firms

Abstract

The backpropagation (BP) learning algorithm is a cornerstone in the training of artificial neural networks (ANNs), widely recognized for its effectiveness in local optimization through iterative adjustments of weights and bias terms. However, in the high-stakes environment of biotechnological financial supervision, the traditional BP-based neural predictive models face challenges such as slow convergence speeds and a high sensitivity to initial model parameters. These limitations are particularly problematic in scenarios requiring rapid model training and deployment. To address these challenges and enhance financial oversight within biotechnological firms, this study explores the integration of advanced AI and deep learning techniques with traditional BP neural networks. We propose a hybrid model that combines Transformer and Multi-Transformer layers with financial analytic techniques like GARCH (Generalized Autoregressive Conditional Heteroskedasticity) algorithms and LSTM (Long Short-Term Memory) units. This innovative approach aims to refine the accuracy of financial predictions and data aggregation models used in the oversight of biotechnological firms. The objective of this research is to develop a robust framework that not only accelerates the training process but also improves the resilience of the model to initial parameter settings, thereby providing biotechnological firms with more reliable tools for financial analysis and decision-making. By enhancing the predictive accuracy of financial models through these hybrid techniques, biotechnological firms can achieve more effective regulatory compliance and risk management, crucial for maintaining competitive advantage and ensuring financial stability.