Fuzzy logic based approaches for gene regulatory network inference
K Raza - Artificial intelligence in medicine, 2019 - Elsevier
The rapid advancements in high-throughput techniques have fueled large-scale production
of biological data at very affordable costs. Some of these techniques are microarrays and …
of biological data at very affordable costs. Some of these techniques are microarrays and …
Using empirical wavelet transform and high-order fuzzy cognitive maps for time series forecasting
HA Mohammadi, S Ghofrani, A Nikseresht - Applied Soft Computing, 2023 - Elsevier
Many studies on time series forecasting have employed fuzzy cognitive maps (FCMs).
However, it is required to develop techniques capable of effective responses and great …
However, it is required to develop techniques capable of effective responses and great …
Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts
K Poczeta, Ł Kubuś, A Yastrebov - Biosystems, 2019 - Elsevier
The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support
systems. It describes the analyzed phenomenon in the form of key concepts and the causal …
systems. It describes the analyzed phenomenon in the form of key concepts and the causal …
Time-series forecasting based on high-order fuzzy cognitive maps and wavelet transform
S Yang, J Liu - IEEE Transactions on Fuzzy Systems, 2018 - ieeexplore.ieee.org
Fuzzy cognitive maps (FCMs) have been successfully used to model and predict stationary
time series. However, it still remains challenging to deal with large-scale nonstationary time …
time series. However, it still remains challenging to deal with large-scale nonstationary time …
Robust empirical wavelet fuzzy cognitive map for time series forecasting
Fuzzy cognitive maps have achieved significant success in time series modeling and
forecasting. However, fuzzy cognitive maps still contain weakness to handle the …
forecasting. However, fuzzy cognitive maps still contain weakness to handle the …
A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps
Z Liu, J Liu - Knowledge-Based Systems, 2020 - Elsevier
Fuzzy cognitive maps (FCMs) have been widely used in time series prediction due to the
excellent performance in dynamic system modeling. However, existing time series prediction …
excellent performance in dynamic system modeling. However, existing time series prediction …
Time series prediction using sparse autoencoder and high-order fuzzy cognitive maps
The problem of time series prediction based on fuzzy cognitive maps (FCMs) is unresolved.
Although many methods have been proposed to cope with this issue, the performance of …
Although many methods have been proposed to cope with this issue, the performance of …
CNN-FCM: System modeling promotes stability of deep learning in time series prediction
P Liu, J Liu, K Wu - Knowledge-Based Systems, 2020 - Elsevier
Time series data are usually non-stationary and evolve over time. Even if deep learning has
been found effective in dealing with sequential data, the stability of deep neural networks in …
been found effective in dealing with sequential data, the stability of deep neural networks in …
Broad fuzzy cognitive map systems for time series classification
Time series classification (TSC) is a crucial and challenging problem in sequential analysis.
However, most of the existing best-performing methods are time-consuming, even if coping …
However, most of the existing best-performing methods are time-consuming, even if coping …
Evolutionary multitasking fuzzy cognitive map learning
In real-world applications, there exist multiple fuzzy cognitive maps (FCMs) learning tasks
with similar attributes that have to be optimized simultaneously, however, all existing …
with similar attributes that have to be optimized simultaneously, however, all existing …