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Takagi–Sugeno–Kang (TSK) fuzzy systems have been widely applied for solving function approximation and regression-centric problems. Existing dynamic TSK models proposed in the literature can be broadly classified into two classes. Class I TSK models are essentially fuzzy systems that are limited to time-invariant environments. Class II TSK models are generally evolving systems that can learn in time-variant...
This paper analyses traffic prediction based on a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network. Traffic prediction is a problem that requires online adaptive systems with high accuracy performance. The proposed GSETSK framework can learn incrementally with high accuracy without any prior assumption about the data sets. To keep an up-to-date fuzzy rule base, a novel ‘gradual’-forgetting-based...
This paper analyses stock market price prediction based on a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network. Stock price prediction is a problem that requires online adaptive systems with high accuracy performance. The proposed GSETSK framework uses a novel Multidimensional-Scaling Growing Clustering (MSGC) algorithm which mimics the human cognitive process to flexibly generate...
The strength of neuro-fuzzy systems involves two contradictory requirements in neuro-fuzzy modeling: interpretability versus accuracy. The Yager-inference-scheme-based fuzzy CMAC (FCMAC-Yager) architecture shows advantages such as it exhibits learning and memory capabilities of the human cerebellum through the CMAC (cerebellar model articulation controller) structure and the human way of reasoning...
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