Design of Multi-Task Parallel Model Based on Fuzzy Neural Networks and Joint Gradient Descent Algorithm

Multi-task learning (MTL) has emerged as a promising paradigm in machine learning, which enables models to generalize better by sharing representations and learning strategies across tasks. However, it may struggle to tune parameters that equally benefit all tasks. To solve this problem, a multi-task parallel model (MTPM) is proposed based on the fuzzy neural networks (FNNs) and the joint gradient descent algorithm (JGDA) that simultaneously optimizes the parameters across parallel tasks. First, an MTPM is constructed using the FNN to extract the interaction features and the specificity features from multiple related tasks. In this model, the shared neurons and specific neurons are embedded into individual FNN rather than single-type neurons, which avoids the conflict of the correlation between tasks and the independence of each task. Second, a JGDA is proposed to achieve the analytical optimization of parameters in the proposed model, including the shared parameters and the specific parameters. The potential correlations between different tasks and the specificity of each task can be shaped to balance knowledge sharing and independent learning. Third, an adaptive learning rate strategy is designed to further improve the model’s performance. In this strategy, the global learning rate (GLR) and task-specific learning rate (SLR) are managed separately, which can improve the model performance by coordinating the training process of different tasks. Finally, the experimental results show that the method proposed in this paper outperforms the traditional method in terms of MTL effects. Compared to single-task benchmark experiments, the model proposed in this paper achieves an average RMSE improvement of 24.2% across three application scenarios, which demonstrates its effectiveness and practicality.