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Rubiyah Yusof

Bio: Rubiyah Yusof is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Feature extraction & PID controller. The author has an hindex of 28, co-authored 221 publications receiving 2960 citations. Previous affiliations of Rubiyah Yusof include Islamic Azad University & University of Tokushima.


Papers
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Journal ArticleDOI
01 Apr 1999
TL;DR: This paper presents a neuro-fuzzy logic controller where all of its parameters can be tuned simultaneously by GA, and shows that the proposed controller offers encouraging advantages and has better performance.
Abstract: Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance.

256 citations

Book
12 Dec 2011
TL;DR: This chapter discusses the application of Neuro-Control to a Water-Bath Process and Comparison with Alternative Control Schemes, and some Discussions on On-Line Learning.
Abstract: 1 Introduction.- 1.1 Introduction to Intelligent Control.- 1.2 References.- 2 Neural Networks.- 2.1 Historical Review of Neural Networks.- 2.2 Backpropagation Algorithm.- 2.2.1 Notation.- 2.2.2 Derivation of the Backpropagation Algorithm.- 2.2.3 Algorithm: Backpropagation Method.- 2.2.4 Some Discussions on the Backpropagation Algorithm.- 2.3 Conclusions.- 2.4 References.- 3 Traditional Control Schemes.- 3.1 Introduction.- 3.2 Discrete-Time PI and PID Controllers.- 3.3 Self-Tuning Control.- 3.4 Self-Tuning PI and PID Controllers.- 3.4.1 Closed Loop System.- 3.4.2 Some Interpretations Based on a Simulation Example.- 3.5 Self-Tuning PID Control - A Multivariable Approach.- 3.5.1 Simulation Example.- 3.6 Generalized Predictive Control - Some Theoretical Aspects.- 3.6.1 Cost Criterion.- 3.6.2 The Plant Model and Optimization Solution.- 3.7 Fuzzy Logic Control.- 3.7.1 Brief Overview of Fuzzy Set and Fuzzy System Theory.- 3.7.2 Basic Concept of Fuzzy Logic Controller.- 3.8 Conclusions.- 3.9 References.- 4 Neuro-Control Techniques.- 4.1 Introduction.- 4.2 Overview of Neuro-Control.- 4.2.1 Neuro-Control Approaches.- 4.2.2 General Control Configuration.- 4.3 Series Neuro-Control Scheme.- 4.4 Extensions of Series Neuro-Control Scheme.- 4.4.1 Some Discussions on On-Line Learning.- 4.4.2 Neuromorphic Control Structures.- 4.4.3 Training Configurations.- 4.4.4 Efficient On-Line Training.- 4.4.5 Training Algorithms.- 4.4.6 Evaluation of the Training.- Algorithms through Simulations.- 4.5 Parallel Control Scheme.- 4.5.1 Learning Algorithm for Parallel Control Scheme.- 4.6 Feedback Error Learning Algorithm.- 4.7 Extension of the Parallel Type Neuro-Controller.- 4.7.1 Description of Control System.- 4.7.2 Linearized Control System.- 4.7.3 Control Systems with Neural Networks.- 4.7.4 Nonlinear Observer by Neural Network.- 4.7.5 Nonlinear Controller by Neural Network.- 4.7.6 Numerical Simulations.- 4.8 Self-Tuning Neuro-Control Scheme.- 4.9 Self-Tuning PID Neuro-Controller.- 4.9.1 Derivation of the Self-Tuning PID Type Neuro-Controller.- 4.9.2 Simulation Examples.- 4.10 Emulator and Controller Neuro-Control Scheme.- 4.10.1 Off-Line Training of the Neuro-Controller and Emulator.- 4.10.2 On-Line Learning.- 4.11 Conclusions.- 4.12 References.- 5 Neuro-Control Applications.- 5.1 Introduction.- 5.2 Application of Neuro-Control to a Water-Bath Process and Comparison with Alternative Control Schemes.- 5.2.1 Introduction.- 5.2.2 Description of the Water Bath Temperature Control System.- 5.2.3 Neuro-Control Scheme.- 5.2.4 Fuzzy Logic Control Scheme.- 5.2.5 Generalized Predictive Control Scheme.- 5.2.6 Experimental Results and Discussions.- 5.2.7 Conclusions.- 5.3 Stabilizing an Inverted Pendulum by Neural Networks.- 5.3.1 Introduction.- 5.3.2 Description of the Inverted Pendulum System.- 5.3.3 Initial Start-Up Control Using Fuzzy Logic.- 5.3.4 Using Optimal Control Strategy for the Stabilization of the Inverted Pendulum.- 5.3.5 Fine Improvement by Using Neural Networks.- 5.3.6 Conclusions.- 5.4 Speed Control of an Electric Vehicle by Self-Tuning PID Neuro-Controller.- 5.4.1 Introduction.- 5.4.2 The Electric Vehicle Control System.- 5.4.3 Self-Tuning PID Type Neuro-Controller.- 5.4.4 Application to Speed Control of Electric Vehicle.- 5.4.5 Conclusions.- 5.5 MIMO Furnace Control with Neural Networks.- 5.5.1 Introduction.- 5.5.2 Description of Furnace Control System.- 5.5.3 The Neuro-Control Scheme.- 5.5.4 Experiments and Discussions.- 5.5.5 Conclusions.- 5.6 Concluding Remarks.- 5.7 References.- Program List.

199 citations

Journal ArticleDOI
04 Jan 2013-Energies
TL;DR: In this paper, the effects of partial shading on the output of photovoltaic (PV) systems are modeled and simulated using MATLAB-programmed modeling and simulation.
Abstract: As of today, the considerable influence of select environmental variables, especially irradiance intensity, must still be accounted for whenever discussing the performance of a solar system. Therefore, an extensive, dependable modeling method is required in investigating the most suitable Maximum Power Point Tracking (MPPT) method under different conditions. Following these requirements, MATLAB-programmed modeling and simulation of photovoltaic systems is presented here, by focusing on the effects of partial shading on the output of the photovoltaic (PV) systems. End results prove the reliability of the proposed model in replicating the aforementioned output characteristics in the prescribed setting. The proposed model is chosen because it can, conveniently, simulate the behavior of different ranges of PV systems from a single PV module through the multidimensional PV structure.

170 citations

01 Jan 2008
TL;DR: This paper designs a proto-type PC-based wood recognition system capable of classifying 30 different tropical Malaysian woods according to their species based on the macroscopic wood anatomy and shows a high rate of recognition accuracy.
Abstract: Tropical rainforest has more than 3,000 different types of timber species. According to the Forest Research Institute of Malaysia, out of these about 200 species are being used by the timber industry. Among the major timber consumers are housing developers, wood fabricators and furniture manufacturers where the need for recognition of wood species is necessary. Automatic wood recognition has not yet been well established mainly due to lack of research in this area and the difficulty in obtaining the wood database. In this paper, an automatic wood recognition system based on image processing, feature extraction and artificial neural networks was designed. The proto-type PC-based wood recognition system is capable of classifying 30 different tropical Malaysian woods according to their species based on the macroscopic wood anatomy. Image processing is carried out using our newly developed in-house image processing library referred to as "Visual System Development Platform". The textural wood features are extracted using a co-occurrence matrix approach, known as grey-level co-occurrence matrix. A multi-layered neural network based on the popular back-propagation algorithm is trained to learn the wood samples for the classification purposes. The system can provide wood identification within seconds, eliminating the need for laborious human recognition. The results obtained show a high rate of recognition accuracy proving that the techniques used is suitable to be implemented for commercial purposes.

142 citations

Journal ArticleDOI
TL;DR: A new hybrid swarm technique (HAP) is used to forecast the energy output of a real wind farm located in Binaloud, Iran and the results indicate that the proposed technique can estimate the output wind power based on the wind speed and the ambient temperature with an MAPE of 3.513%.

112 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations

Journal ArticleDOI
TL;DR: A survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models.
Abstract: Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

1,575 citations

Journal ArticleDOI
TL;DR: The proposed PSO method was indeed more efficient and robust in improving the step response of an AVR system and had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.
Abstract: In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.

1,485 citations