Nonlinear auto-regressive model structure selection using binary particle swarm optimization algorithm / Ahmad Ihsan Mohd Yassin

System Identification (SI) is a control engineering discipline concerned with the discovery of mathematical models based on dynamic measurements collected from the system. It is an important discipline in the construction and design of controllers, as SI can be used for understanding the properties of the system as well as to forecast its behavior under certain past inputs and/or outputs. The NARMAX model and its derivatives (Nonlinear Auto-Regressive with Exogenous Inputs (NARX) and Nonlinear Auto-Regressive Moving Average (NARMA)) are powerful, efficient and unified representations of a variety of nonlinear models. The identification process of NARX/NARMA/NARMAX involves structure selection and parameter estimation, which can be simultaneously performed using the widely accepted Orthogonal Least Squares (OLS) algorithm. Several criticisms have been directed towards OLS for its tendency to select excessive or sub-optimal terms. The suboptimal selection of regressor terms leads to models that are non-parsimonious in nature. This thesis proposes the application of a stochastic optimization algorithm called Binary Particle Swarm Optimization algorithm for structure selection of polynomial NARX/NARMA/NARMAX models. The algorithm searches the solution space by selecting various model structures and evaluating its fitness. A MySQL database was created to analyze the optimization results and speed up computations of the optimization algorithm. The proposed optimization algorithm was tested on several benchmark datasets, namely the Direct Current Motor (DCM), Mackey-Glass Differential Equation (MG) and Flexible Robot Arm (FRA). The DCM motor was the least complication dataset, followed by the FRA (medium complexity) and MG (most complexity). The results suggest that the proposed method can reduce the number of correlation violations down to between 28.57% and 69.23% at the expense of increased model size (requirement of additional regressor terms to explain the behavior of the system)

The Performance of Binary Artificial Bee Colony (BABC) in Structure Selection of Polynomial NARX and NARMAX Models

This paper explores the capability of the Binary Artificial Bee Colony (BABC) algorithm for feature selection of Nonlinear Autoregressive Moving Average with Exogenous Inputs (NARMAX) model, and compares its implementation with the Binary Particle Swarm Optimization (BPSO) algorithm. A binarized modification of the BABC algorithm was used to perform structure selection of the NARMAX model on a Flexible Robot Arm (FRA) dataset. The solution quality and convergence was compared with the BPSO optimization algorithm. Fitting and validation tests were performed using the One-Step Ahead (OSA), correlation and histogram tests. BABC was able to outperform BPSO in terms of convergence consistency with equal solution quality. Additionally, it was discovered that BABC was less prone to converge to local minima while BPSO was able to converge faster. Results from this study showed that BABC was better-suited for structure selection in huge dataset and the convergence has been proven to be more consistent relative to BPSO

The Performance of Artificial Bee Colony (ABC) in Structure Selection of Polynomial NARX Models

System Identification (SI) is a discipline of building a mathematical model of dynamic systems based on its input and output data. The process of SI is generally divided into structure selection, parameter estimation and model validation. This paper attempts to address the structure selection issue in SI, where the objective is to select the most representative set of regressors to represent the system. However, the selection process must obey the principle of parsimony, where the structure must be as small as possible, yet has the ability to represent the system well. We propose a binarized modification of the Artificial Bee Colony (ABC) algorithm to perform structure selection of a Nonlinear Auto-Regressive with eXogenous (NARX) model on a Direct Current (DC) motor. We compare this implementation with the Binary Particle Swarm Optimization (BPSO) algorithm in terms of solution quality and convergence consistency. The results indicate that the ABC algorithm excelled in terms of convergence consistency with similar solution quality to BPSO algorithm

EMG Signals Analysis of BF and RF Muscles In Autism Spectrum Disorder (ASD) During Walking

This paper presents the analysis of Electromyography (EMG) signals at lower limb muscles during walking. The muscles of Biceps Femoris (BF) and Rectus Femoris (RF) were examined between ASD and TD children. The EMG signals pattern will be observed over one gait cycle and the statistical analysis will be used to compare the significant difference of two muscles between ASD and TD children. The result shows that there are significant differences in RF muscle for both ASD and TD children at 70% of gait cycle (p value is equal to 0.007) and at 90% of gait cycle (p value is equal to 0.023). From this result, the RF muscle shall be considered as the vital muscle for rehabilitation plan

Integration of CompTIA Cloud+ into Universiti Teknologi MARA’s Computer Engineering Special Topics Syllabus

Cloud computing is one of the frontier technologies in computer engineering. Therefore, it is important to prepare the students with the knowledge and skills required in this field. This paper describes the integration of the renowned CompTIA Cloud+ professional training course into the ECE648: Special Topics in Computer Networking subject. The subject is a final year elective for the Faculty of Electrical Engineering, Universiti Teknologi MARA, Malaysia undergraduate computer engineering students. We first begin by assessing the current teaching syllabus of a variety of international universities to establish fundamental topics that should be covered in a cloud computing course. We then proceed to describe our implementation, which is done in accordance with the CompTIA Cloud+ certification syllabus. Among the items described are how the Cloud+ course contents are adjusted to be more suitable for the course, as well as additional practical elements (not originally available in the Cloud+ course) added to the course syllabus. Implementation results are described

Non-Linear Autoregressive with exogenous input (Narx) chiller plant prediction model

A chiller plant is a centralized system used for air cooling systems, commonly, for covering a large area of building with various components such as chillers, cooling towers, pumps, and chilled water storage tanks. Each component has several sensors or indicators with status information. Users can use the information to plan for maintenance and as guidance during troubleshot if an event occurs. It is crucial to ensure the chiller plant is operating efficiently without any faulty especially in critical buildings such as a hospital. The main problem of the chiller plant is to conduct preventive maintenance for avoiding the chiller plant failure and breakdown unexpectedly. Based on the literature, approximately 80 components in the chiller plant has found as the possible reason for the chiller plant faulty. In the current research, modeling chiller plants has been done by several researchers, objectively for preventative maintenance purposes. Study case for this project is for a chiller plant at Hospital Raja Permaisuri Bainun, Ipoh, Perak, Malaysia. A model for the proposed chiller plant system is to be designed using System Identification (SI) technique based on Nonlinear Autoregressive with Exogenous Inputs (NARX). Validation result shows, the proposed chiller plant system can be modelled and to be used as One Step Ahead prediction tool with residual Mean Square Error (MSE) of 1.018E-3 for training set and 1.017E-3 for testing set

Developing multi-tier network design for effective energy consumption of cluster head selection in WSN / Wan Isni Sofiah Wan Din … [et al.]

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy