In this work a Generalized type-2 Fuzzy Logic System (GT2FLS) approach for dynamic parameter adaptation in metaheuristics and for optimal fuzzy controller design is presented. In these two cases, the ...efficiency of the GT2FLS approach is verified with simulation results. In the first case, the GT2FLS provides an approach to dynamically find the optimal values of the heuristic parameters that are a critical part of the Bee Colony Optimization (BCO) algorithm performance. In the second case, the GT2FLS approach provides the basis for building a Generalized type-2 Fuzzy Logic Controller (GT2FLC), which can be optimized with the traditional BCO, specifically to find the optimal design of the Membership Functions (MFs) in the Fuzzy Controller. In both cases, the GT2FLS approach shows advantages in the optimization of the solutions to the problems. For both cases, we can considered them as hybrid systems combining GT2FLS and BCO although the combination is made in a different way, and it can be noted that a GT2FLS presents better stability in the minimization of the errors when applied to benchmark control problems. Simulation results illustrate that the implementation of the Generalized type-2 Fuzzy Logic Controller (GT2FLC) approach improves its performance when using the BCO algorithm and the stability of the fuzzy controller is better when compared with respect to a type-1 Fuzzy Logic Controller (T1FLC) and an Interval type-2 Fuzzy Logic Controller (IT2FLC).
•In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented.•Recently, type-2 fuzzy logic has gained popularity in a wide ...range of applications due to its ability to handle higher degrees of uncertainty.•In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classification and clustering, where it has helped improving results over type-1 fuzzy logic.•In this paper a concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented.
In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented. Recently, type-2 fuzzy logic has gained popularity in a wide range of applications due to its ability to handle higher degrees of uncertainty. In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classification and clustering, where it has helped improving results over type-1 fuzzy logic. In this paper a concise and representative review of the most successful applications of type-2 fuzzy logic in these fields is presented. At the moment, most of the applications in this review use interval type-2 fuzzy logic, which is easier to handle and less computational expensive than generalized type-2 fuzzy logic.
The electrocatalytic reduction of CO2 has been investigated using four Cu‐based metal–organic porous materials supported on gas diffusion electrodes, namely, (1) HKUST‐1 metal–organic framework ...(MOF), Cu3(μ6‐C9H3O6)2n; (2) CuAdeAce MOF, Cu3(μ3‐C5H4N5)2n; (3) CuDTA mesoporous metal–organic aerogel (MOA), Cu(μ‐C2H2N2S2)n; and (4) CuZnDTA MOA, Cu0.6Zn0.4(μ‐C2H2N2S2)n. The electrodes show relatively high surface areas, accessibilities, and exposure of the Cu catalytic centers as well as favorable electrocatalytic CO2 reduction performance, that is, they have a high efficiency for the production of methanol and ethanol in the liquid phase. The maximum cumulative Faradaic efficiencies for CO2 conversion at HKUST‐1‐, CuAdeAce‐, CuDTA‐, and CuZnDTA‐based electrodes are 15.9, 1.2, 6, and 9.9 %, respectively, at a current density of 10 mA cm−2, an electrolyte‐flow/area ratio of 3 mL min cm−2, and a gas‐flow/area ratio of 20 mL min cm−2. We can correlate these observations with the structural features of the electrodes. Furthermore, HKUST‐1‐ and CuZnDTA‐based electrodes show stable electrocatalytic performance for 17 and 12 h, respectively.
Closing the loop: Metal–organic porous materials are effective electrocatalysts for the continuous electrochemical conversion of CO2 to alcohols, a process that could promote the transition to a low‐carbon economy. The modularity of these systems yields many opportunities for further performance improvements and opens new directions in electrocatalysis.
In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are ...used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.
We outline in this article a hybrid intelligent fuzzy fractal approach for classification of countries based on a mixture of fractal theoretical concepts and fuzzy logic mathematical constructs. The ...mathematical definition of the fractal dimension provides a way to estimate the complexity of the non-linear dynamic behavior exhibited by the time series of the countries. Fuzzy logic offers a way to represent and handle the inherent uncertainty of the classification problem. The hybrid intelligent approach is composed of a fuzzy system formed by a set of fuzzy rules that uses the fractal dimensions of the data as inputs and produce as a final output the classification of countries. The hybrid approach calculations are based on the COVID-19 data of confirmed and death cases. The main contribution is the proposed hybrid approach composed of the fractal dimension definition and fuzzy logic concepts for achieving an accurate classification of countries based on the complexity of the COVID-19 time series data. Publicly available datasets of 11 countries have been the basis to construct the fuzzy system and 15 different countries were considered in the validation of the proposed classification approach. Simulation results show that a classification accuracy over 93% can be achieved, which can be considered good for this complex problem.
A new method for finding fuzzy information granules from multivariate data through a gravitational inspired clustering algorithm is proposed in this paper. The proposed algorithm incorporates the ...theory of granular computing, which adapts the cluster size with respect to the context of the given data. Via an inspiration in Newton’s law of universal gravitation, both conditions of clustering similar data and adapting to the size of each granule are achieved. This paper compares the Fuzzy Granular Gravitational Clustering Algorithm (FGGCA) against other clustering techniques on two grounds: classification accuracy, and clustering validity indices, e.g. Rand, FM, Davies–Bouldin, Dunn, Homogeneity, and Separation. The FGGCA is tested with multiple benchmark classification datasets, such as Iris, Wine, Seeds, and Glass identification.
•A hybrid mathematical model of Zika virus with optimal control strategies is formulated.•The qualitative study of stability analysis was evaluated at different possible equilibria and reproduction ...number R0 is computed by using theory of stability analysis.•The analysis of model suggests various strategies which help in the elimination of the disease.•The implementation of an optimal control intervenes in terms of vaccination to control the dynamics of the deadly virus could be effective and mitigated the number of infected population suffered from virus in short span of time.
Zika virus is amongst the deadly viruses still prevalent in more than 50 countries around the world. It is amosquitoes-borne disease that spread at fast rate in 2016. Zika virus belongs to the family of Flaviviridae virus. In this paper, a deterministic mathematical model is formulated to investigate the effects of vaccination in controlling the disease through optimal solution. The qualitative behavior of the proposed model is studied by using the theory of stability analysis. A basic reproduction number is computed by using Next Generation Technique todecide a threshold values of R0i.e. if R0<1,the system is locally asymptotically stable and disease dies out and ifR0>1 dynamicsthe system is locally asymptotically unstable and diseases persist in the system. The global stability is also investigated via Lyapunov function. Numerical results were performed to see the effects of vaccination on the dynamics of the model with and without optimal interventions. The analysis of model suggests various strategies which help in the elimination of the disease.
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