Add to ORKGFuzzy neural technique is often used to model dynamic data stream in the financial market and examines hypothetical cases. Hence, Fuzzy interpolation and extrapolation are required if the data is sparse, particularly in financial option trading. However, many of them do not have the learning ability. This paper extends the work of HS [103] with on-line learning ability. The result enables both interpolation and extrapolation to be applied in the neuro-fuzzy system algorithm in order to make inference when drift is detected or in sparse rule–based systems. This paper proposes a novel neuro-fuzzy system architecture called evolving Mamdani Fuzzy Inference System with Fuzzy Rule interpolation or Extrapolation (eMFIS (FRI/E)) that has the fol...
The prediction of financial time series is a very complicated process. If the efficient market hypot...
This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzz...
Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion...
Neuro-fuzzy systems (NFS) are hybrid systems which benefit from the expressive IF-THEN fuzzy rules a...
Fuzzy neural networks are often used for modelling dynamic data streams and the systems keep evolvin...
Fuzzy techniques have been studied for implementation in neural networks to better model the nature ...
Fuzzy neural networks are often used to handle dynamic data stream in the financial market. However,...
This thesis presents a novel neural-fuzzy network architecture named the evolving Mamdani-Takagi-Sug...
One of the hardest challenges for machine learning models in finance, medicine, engineering, and sci...
Neuro-fuzzy system, traditionally used in dynamic data sets modelling, is now evolving rapidly in bo...
Fuzzy logic systems can broadly be grouped into two main types; namely: linguistic fuzzy systems (Ma...
Many existing neural fuzzy systems are capable of self-learning and adapt their initial structure as...
Machine learning models can be used in fields like finance, engineering, medicine and science to mak...
Neural fuzzy system is a hybrid intelligent system that synergizes artificial neural network and fuz...
Neuro-fuzzy systems are hybrid systems that take advantage on the functionalities of fuzzy logics an...
The prediction of financial time series is a very complicated process. If the efficient market hypot...
This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzz...
Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion...
Neuro-fuzzy systems (NFS) are hybrid systems which benefit from the expressive IF-THEN fuzzy rules a...
Fuzzy neural networks are often used for modelling dynamic data streams and the systems keep evolvin...
Fuzzy techniques have been studied for implementation in neural networks to better model the nature ...
Fuzzy neural networks are often used to handle dynamic data stream in the financial market. However,...
This thesis presents a novel neural-fuzzy network architecture named the evolving Mamdani-Takagi-Sug...
One of the hardest challenges for machine learning models in finance, medicine, engineering, and sci...
Neuro-fuzzy system, traditionally used in dynamic data sets modelling, is now evolving rapidly in bo...
Fuzzy logic systems can broadly be grouped into two main types; namely: linguistic fuzzy systems (Ma...
Many existing neural fuzzy systems are capable of self-learning and adapt their initial structure as...
Machine learning models can be used in fields like finance, engineering, medicine and science to mak...
Neural fuzzy system is a hybrid intelligent system that synergizes artificial neural network and fuz...
Neuro-fuzzy systems are hybrid systems that take advantage on the functionalities of fuzzy logics an...
The prediction of financial time series is a very complicated process. If the efficient market hypot...
This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzz...
Hybrid algorithm is the hot issue in Computational Intelligence (CI) study. From in-depth discussion...