Sliding Mode Control of Interval Type-2 T-S Fuzzy Systems with Redundant Channels

This paper investigates the sliding mode control (SMC) of interval type-2 (IT2) T-S fuzzy systems. The measurement outputs are propagated via redundant channels for reducing the probability of packet loss and improving the reliability of data transmission. A key feature for the above problem is that the premise variables and the measurement signals may not be available by the controller, which brings difficulty to stabilize the nonlinear systems. Accordingly, a crucial issue is how to synthesize an implementable SMC law under the redundant channels. To this end, the characteristic of the redundant channels is firstly analyzed and the model of available measurement output signals is established. By employing these available measurements as the premise variables and utilizing the upper and lower bounds of the system membership functions (MFs), new MFs are constructed and the sliding mode controller is synthesized. By introducing some null terms carrying the information of MFs, sufficient conditions are derived in terms of nonlinear matrix inequalities to ensure the stochastically ultimate boundedness of the closed-loop system and the reachability of the specified sliding surface. Besides, a binary genetic algorithm (GA) is introduced to solve the nonlinear criteria via the objective function reflecting the control performance. Finally, a numerical example illustrates the effectiveness of the proposed methods.

A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification

A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional biomedical datasets are used to test the ability of MSBGWO in feature selection. The experimental results of MSBGWO are superior in terms of classification accuracy, precision, recall, F -measure, and number of features selected when compared to those of the binary grey wolf optimizer version 2 (BGWO2), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and sine-cosine algorithm (SCA).

Enhanced Binary Genetic Algorithm as a Feature Selection to Predict Student Performance

Students’ performance prediction systems play a vital role in enhancing the educational performance inside universities, schools, and training centers. Big data can come from different resources such as exam centers, virtual courses, registration departments, e-learning systems, and so on. Extracting meaningful knowledge from educational data is a complex task, so, reducing the data dimensionality is needed. In this paper, we proposed an enhanced binary genetic algorithm (EBGA) as a wrapper feature selection algorithm. Novel hybrid selection mechanism based on a k-means algorithm and Electromagnetic-like mechanism (EM) method is proposed. K-means will cluster the population into a set of clusters, while EM will determine a value called a total force (TF) for each solution. Each cluster has an accumulated total force (ATF) (i.e., adding all TFs together). Selection process will select two solutions with the highest TF from the cluster, which has the highest ATF. We employed a hybrid machine learning approach between the proposed EBGA and five different classifiers (i.e., k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)). Two real case studies obtained from UCI Machine Learning Repository are used in this paper. Obtained results showed the ability of the proposed approach to enhance the performance of the binary genetic algorithm. Moreover, the performances of all classifiers are improved between 1% to 11%.

A Low-Profile HF Meandered Dipole Antenna with a Ferrite-Loaded Artificial Magnetic Conductor

In this paper, a low-profile HF (high-frequency) meandered dipole antenna with a ferrite-loaded artificial magnetic conductor (AMC) is proposed. To operate in the HF band while retaining a compact size, ferrite with high permeability is applied to the unit cell of the AMC. The operating frequency bandwidth of the designed unit cell of the AMC is 1.89:1 (19–36 MHz). Thereafter, a meandered dipole antenna is designed by implementing a binary genetic algorithm and is combined with the AMC. The overall size of the designed antenna is 0.06×0.06×0.002 λ3 at the lowest operating frequency. The proposed dipole antenna with a ferrite-loaded AMC is fabricated and measured. The measured VSWR bandwidth (<3) covers 20–30 MHz on the HF band. To confirm the performance of the antenna, a reference monopole antenna which operates on the HF band was selected, and the measured receiving power is compared with the result of the proposed antenna with the AMC.