FE-RNN: a fuzzy embedded recurrent neural network for improving interpretability of underlying neural network - Tan, James Chee Min (2024)
Deep learning enables effective predictions. But deep structures face some challenges on human interpretability compared to conventional techniques, e.g., fuzzy inference systems. It motivates more research works to alleviate the black box nature of deep structures with performance maintained. This paper proposes a fuzzy-embedded recurrent neural network (FE-RNN) to improve interpretability of the underlying neural networks. It is a parallel deep structure comprising an RNN and a Pseudo Outer-Product based Fuzzy Neural Network (POPFNN) that share a common set of input and output linguistic concepts. The inference processes undertaken are associated by RNN using fuzzy rules in the embedded POPFNN. Fuzzy IF-THEN rules provide better interpretability of the inference process of the hybrid networks. It allows an effective realisation of a data driven implication using RNN in the modelling of fuzzy entailment within a fuzzy neural networks (FNN) structure. FE-RNN obtains more consistent results than other FNN in the experiment using the Mackey-Glass dataset. FE-RNN achieves about 99% correlation for forecasting prices of market indexes. Its interpretability is also discussed. FE-RNN then acts as a prediction tool in a financial trading system using forecast-assisted technical indicators optimised with Genetic Algorithms. It outperforms the benchmark trading strategies in the trading experiments.