Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization

In the present study, a novel neuro-controller is suggested for hard disk drive (HDD) systems in addition to nonlinear dynamic systems using the Multifeedback-Layer Neural Network (MFLNN) proposed in recent years. In neuro-controller design problems, since the derivative based train methods such as the back-propagation and Levenberg-Marquart (LM) methods necessitate the reference values of the neural network’s output or Jacobian of the dynamic system for the duration of the train, the connection weights of the MFLNN employed in the present work are updated using the Particle Swarm Optimization (PSO) algorithm that does not need such information. The PSO method is improved by some alterations to augment the performance of the standard PSO. First of all, this MFLNN-PSO controller is applied to different nonlinear dynamical systems. Afterwards, the proposed method is applied to a HDD as a real system. Simulation results demonstrate the effectiveness of the proposed controller on the control of dynamic and HDD systems

Time series prediction and channel equalizer using artificial neural networks with VLSI implementation

The architecture and training procedure of a novel recurrent neural network (RNN), referred to as the multifeedbacklayer neural network (MFLNN), is described in this paper. The main difference of the proposed network compared to the available RNNs is that the temporal relations are provided by means of neurons arranged in three feedback layers, not by simple feedback elements, in order to enrich the representation capabilities of the recurrent networks. The feedback layers provide local and global recurrences via nonlinear processing elements. In these feedback layers, weighted sums of the delayed outputs of the hidden and of the output layers are passed through certain activation functions and applied to the feedforward neurons via adjustable weights. Both online and offline training procedures based on the backpropagation through time (BPTT) algorithm are developed. The adjoint model of the MFLNN is built to compute the derivatives with respect to the MFLNN weights which are then used in the training procedures. The Levenberg–Marquardt (LM) method with a trust region approach is used to update the MFLNN weights. The performance of the MFLNN is demonstrated by applying to several illustrative temporal problems including chaotic time series prediction and nonlinear dynamic system identification, and it performed better than several networks available in the literature

Real-time multimodal emotion classification system in E-Learning context

Emotions of learners are crucial and important in e-learning as they promote learning. To investigate the effects of emotions on improving and optimizing the outcomes of e-learning, machine learning models have been proposed in the literature. However, proposed models so far are suitable for offline mode, where data for emotion classification is stored and can be accessed boundlessly. In contrast, when data arrives in a stream, the model can see the data once and real-time response is required for real-time emotion classification. Additionally, researchers have identified that single data modality is incapable of capturing the complete insight of the learning experience and emotions. So, multi-modal data streams such as electroencephalogram (EEG), Respiratory Belt (RB), electrodermal activity data (EDA), etc., are utilized to improve the accuracy and provide deeper insights in learners’ emotion and learning experience. In this paper, we propose a Real-time Multimodal Emotion Classification System (ReMECS) based on Feed-Forward Neural Network, trained in an online fashion using the Incremental Stochastic Gradient Descent algorithm. To validate the performance of ReMECS, we have used the popular multimodal benchmark emotion classification dataset called DEAP. The results (accuracy and F1-score) show that the ReMECS can adequately classify emotions in real-time from the multimodal data stream in comparison to the state-of-the-art approaches.Work partially funded by ACCIO under the project TutorIA.Peer ReviewedPostprint (author's final draft

Core power control analysis and design for triga nuclear reactor

An efficient nuclear core power control is essential in providing a safe and reliable nuclear power generation system. It is technically challenging to ensure that the core power output is always stable and operating within acceptable error bands. The core power control in TRIGA PUSPATI Reactor (RTP) Malaysia is designed based on the Feedback Control Algorithm (FCA), which includes the Proportional- Integral controller, Control Rod Selection Algorithm (CRSA), Control Rod Velocity Design (CRVD), and Power Change Rate Constraint (PCRC). However, the current setting generally produces an unsmooth transient response and a long settling time. The conventional CRSA suffers during transient and fine-tuning conditions due to the rod selection process only considers the rod position and ignores the rod worth value. The conventional PCRC has a constant gain, incapable of providing a sufficient amount of penalty and sensitivity effects on control rod velocity under all operating conditions. Thus, a new strategy for each component in the FCA is investigated to further improve overall core power tracking performance. To address the current CRSA problems, a novel CRSA called Single Control Absorbing Rod (SCAR) is designed based on the rod worth value and operational condition-based activation. The SCAR is not only reducing the complexity of the CRSA process but also reduces the time required for rod selection. In addition, a new saturation model and velocity value are studied for CRVD. On top of that, a fuzzy-based PCRC is proposed to produce a fast-tracking power response. Finally, a hybrid controller based on the integration of Model Predictive Control and Proportional controller is developed to exploit the benefits of both controllers via a switching control mechanism. In the present study, the RTP model is derived based on equations of neutronic, thermal-hydraulic, reactivity, and dynamic rod position. Both analytical and system identification models are considered. In the proposed design strategy, all of the safety design requirements based on the Final Safety Analysis Report are taken into account, ensuring that the outcome of the study is practical and reliable. The proposed strategy is designed via simulation with MATLAB Simulink and experimentation with actual hardware at the RTP. A stability analysis based on Lyapunov is derived to numerically guarantee the stability of the new power controller. An extensive comparison to the existing FCA is presented to demonstrate the compatibility and effectiveness of the proposed strategies in nuclear reactor environments. Overall, the results show that the response from hybrid Model Predictive Control-Proportional (MPC-P) offers better results than the FCA, in which reduces the rise time by up to 73 %, the settling time by up to 70 %, and the workload by up to 42 %. The hybrid MPC-P with multiple-component constraints is able to solve the unsmooth transient response and a long settling time tracking performance at the RTP and offers improvements in terms of fuel economic aspect in the long run and extending the lifetime of the plant operation

Smart grid framework analysis and artificial neutral network in load forecast

Power system is the one of the most critical parts of the whole energy utilization around the world. Recently people pay more attention to the energy utilization, new types of generations, storages and power utilization need to increase energy efficiency and reduce carbon emission. Due to the power grid currently is still mainly under the old-designed approach, it is increasingly exposed limitation on efficiency enhancement, security and reliability improvement, new technologies compatibility and meeting larger power capacity requirements. Thus, Smart Grid is 'born' to improve power grid for these requirements. It is an overlapping area between power system and digital technology, intelligent technology, communication technology and so on. Smart Grid can provide updates for nearly all sections of traditional power grid. It is a systematic framework that new technologies integration, system development strategy and planning, customers' awareness improvements and supports from all relevant areas. The areas must be operated in coordination and parallel. Firstly, this thesis introduces Smart Grid and Smart Metering on its definition, characteristics and deployment. Secondly, this thesis describes a load forecasting system for macro-grid. Artificial Neural Network (ANN) was introduced to achieve this work for its excellent mapping approximation ability. In the third section, thesis focuses on load forecasting for micro-grid. BackPropagation method is used to train the Multi-layer Perceptron (MLP) ANN and its results were compared to that from Radial Basis Function (RBF) ANN. Analysis was focused not only on the two networks but also ANN generalization problems and differences between micro-grid load and macro-grid load prediction

Identification of disk drive systems using the Multifeedback-Layer Neural Network and the Particle Swarm Optimization algorithm

2013 International Conference on Technological Advances in Electrical, Electronics and Computer Engineering, TAEECE 2013 --9 May 2013 through 11 May 2013 -- Konya --In this study, the closed loop identification of the reader head position of a disk drive system is proposed by using the Multifeedback-Layer Neural Network. To identify the system, the connection weights of the Multifeedback-Layer Neural Network (MFLNN) are trained by the Particle Swarm Optimization (PSO) algorithm. Simulation results show the effectiveness of the proposed method. © 2013 IEEE

Multifeedback-layer neural network

WOS: 000244970900005PubMed ID: 17385626The architecture and training procedure of a novel recurrent neural network (RNN), referred to as the multifeedback-layer neural network (MFLNN), is described in this paper. The main difference of the proposed network compared to the available RNNs is that the temporal relations are provided by means of neurons arranged in three feedback layers, not by simple feedback elements, in order to enrich the representation capabilities of the recurrent networks. The feedback layers provide local and global recurrences via nonlinear processing elements. In these feedback layers, weighted sums of the delayed outputs of the hidden and of the output layers are passed through certain activation functions and applied to the feedforward neurons via adjustable weights. Both online and offline training procedures based on the backpropagation through time (BPTT) algorithm are developed. The adjoint model of the MFLNN is built to compute the derivatives with respect to the MFLNN weights which are then used in the training procedures. The Levenberg-Marquardt (LM) method with a trust region approach is used to update the MFLNN weights. The performance of the MFLNN is demonstrated by applying to several illustrative temporal problems including chaotic time series prediction and nonlinear dynamic system identification, and it performed better than several networks available in the literature

Training the multifeedback-layer neural network using the Particle Swarm Optimization algorithm

2013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013 --7 November 2013 through 8 November 2013 -- Ankara --In this study, the Multifeedback-Layer Neural Network (MFLNN) weights are trained by the Particle Swarm Optimization (PSO). This method (MFLNN-PSO) is applied to two different problems to prove accomplishment of the study. Firstly, a chaotic time series prediction problem is used to test the MFLNN-PSO. Also, the method is used for identification of a non-linear dynamic system. This study shows that the MFLNN-PSO can be used for dynamic system identification as well as controller design. © 2013 IEEE