TSK-Streams: learning TSK fuzzy systems for regression on data streams

Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems

This work is focused on creating fuzzy granular classification models based on general type-2 fuzzy logic systems when consequents are represented by interval type-2 TSK linear functions. Due to the complexity of general type-2 TSK fuzzy logic systems, a hybrid learning approach is proposed, where the principle of justifiable granularity is heuristically used to define an amount of uncertainty ...

Learning Accurate TSK Models Based on Local Fuzzy Prototyping

This work presents the use of local fuzzy prototypes as a first approximation to obtain accurate local semantics-based TakagiSugeno-Kang rules. A two-stage evolutionary algorithm considering the interaction between input and output variables has been developed. Firstly, it performs a local identification of prototypes, and then, a post-processing stage is considered to refine them. The proposal...

A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the ident...

Genetic Programming for Model Selection of TSK-Fuzzy Systems

Simulation Modeling of TSK Fuzzy Systems for Model Continuity

This paper presents an approach to formally model Takagi–Sugeno– Kang (TSK) fuzzy systems without the use of any external components. In order to keep the model continuity, the formal simulation model for a TSK fuzzy system is comprised of three types of reusable sub-models involving primitive operations. Thus, the model can be executed even on limited computational platforms, such as embedded ...