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.

Fuzzy rule interpolation forms an important approach for performing inference with systems comprising sparse rule bases. Even when a given observation has no overlap with the antecedent values of any existing rules, fuzzy rule interpolation may still derive a useful conclusion. Unfortunately, very little of the existing work on fuzzy rule interpolation can conjunctively handle more than one form of uncertainty in the rules or observations. In particular, the difficulty in defining the required precise-valued membership functions for the fuzzy sets that are used in conventional fuzzy rule interpolation techniques significantly restricts their application. In this paper, a novel rough-fuzzy approach is proposed in an attempt to address such difficulties. The proposed approach allows the representation, handling and utilisation of different levels of uncertainty in knowledge. This allows transformation-based fuzzy rule interpolation techniques to model and harness additional uncertain information in order to implement an effective fuzzy interpolative reasoning system. Final conclusions are derived by performing rough-fuzzy interpolation over this representation. The effectiveness of the approach is illustrated by a practical application to the prediction of diarrhoeal disease rates in remote villages. It is further evaluated against a range of other benchmark case studies. The experimental results confirm the efficacy of the proposed work.