Journal ArticleDOI
An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization
TLDR
The proposed novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC.Abstract:
This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.read more
Citations
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Journal ArticleDOI
Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with Interval Type-2 and Type-1 Fuzzy Systems
TL;DR: Simulation results show that Generalized Type-2 Fuzzy Controllers outperform their Type-1 and Interval Type- 2 FBuzzy Controller counterparts in the presence of external perturbations.
Journal ArticleDOI
Fault Detection for T-S Fuzzy Time-Delay Systems: Delta Operator and Input-Output Methods
TL;DR: By designing a filter to generate a residual signal, the fault detection problem addressed in this paper can be converted into a filtering problem and the time-varying delay is approximated by the two-term approximation method.
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TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds
TL;DR: The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.
Journal ArticleDOI
Recurrent Broad Learning Systems for Time Series Prediction
TL;DR: A novel recurrent BLS with sparse autoencoder used to extract the features from the input instead of the randomly initialized weights, motivated by the idea of “fine-tuning” in deep learning.
Journal ArticleDOI
A Review of Deep Learning Models for Time Series Prediction
TL;DR: This paper reviews the state of the art developments in deep learning for time series prediction and categorizes them into discriminative, generative, and hybrids models, based on modeling for the perspective of conditional or joint probability.
References
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