RSPOP: set-based pseudo outer-product fuzzy rule identification algorithm. System modeling with neuro-fuzzy systems involves two contradictory requirements: interpretability verses accuracy. The pseudo outer-product (POP) rule identification algorithm used in the family of pseudo outer-product-based fuzzy neural networks (POPFNN) suffered from an exponential increase in the number of identified fuzzy rules and computational complexity arising from high-dimensional data. This decreases the interpretability of the POPFNN in linguistic fuzzy modeling. This article proposes a novel rough set-based pseudo outer-product (RSPOP) algorithm that integrates the sound concept of knowledge reduction from rough set theory with the POP algorithm. The proposed algorithm not only performs feature selection through the reduction of attributes but also extends the reduction to rules without redundant attributes. As many possible reducts exist in a given rule set, an objective measure is developed for POPFNN to correctly identify the reducts that improve the inferred consequence. Experimental results are presented using published data sets and real-world application involving highway traffic flow prediction to evaluate the effectiveness of using the proposed algorithm to identify fuzzy rules in the POPFNN using compositional rule of inference and singleton fuzzifier (POPFNN-CRI(S)) architecture. Results showed that the proposed rough set-based pseudo outer-product algorithm reduces computational complexity, improves the interpretability of neuro-fuzzy systems by identifying significantly fewer fuzzy rules, and improves the accuracy of the POPFNN.

References in zbMATH (referenced in 8 articles , 1 standard article )

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  1. Namdari, Alireza; Durrani, Tariq S.: A multilayer feedforward perceptron model in neural networks for predicting stock market short-term trends (2021)
  2. Novak, Marija Gorenc; Velušček, Dejan: Prediction of stock price movement based on daily high prices (2016)
  3. Tung, S. W.; Quek, C.; Guan, C.: eT2FIS: an evolving type-2 neural fuzzy inference system (2013) ioport
  4. Quek, C.; Pasquier, M.; Kumar, N.: A novel recurrent neural network-based prediction system for option trading and hedging (2008) ioport
  5. Tan, A.; Quek, C.; Yow, K. C.: Maximizing winning trades using a novel RSPOP fuzzy neural network intelligent stock trading system (2008) ioport
  6. Liu, Feng; Quek, Chai; Ng, Geok See: A novel generic Hebbian ordering-based fuzzy rule base reduction approach to Mamdani neuro-fuzzy system (2007)
  7. Tung, W. L.; Quek, C.: A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty (2007) ioport
  8. Ang, Kai Keng; Quek, Chai: RSPOP: set-based pseudo outer-product fuzzy rule identification algorithm (2005)