A neuro-fuzzy controller for a stoker-fired boiler,based on behavior modeling

A key issue in an industrial stoker-fired boiler is the design of an efficient and robust controller for its combustion system, so that the boiler can provide a continuous supply of steam at the desired pressure conditions. However, it is difficult to achieve this objective by using a model-based approach because of the high nonlinearity and uncertainty of boiler systems. In addition, the control performance may also suffer as a result of strong load changes, large disturbances, large time lags, and so forth. This paper presents a behavior-modeling-based approach to the design of a neuro-fuzzy controller for the combustion control of a stoker-fired boiler. In this approach, boiler combustion processes with unknown structure are modeled by defining three dynamic behaviors. According to these behavior ‘templates’, their corresponding fuzzy-logic controllers can be optimized off-line. During boiler system operation, the appropriate fuzzy-logic controller is fired, based on an on-line assessment of its dynamic behavior. The application results obtained demonstrate the effectiveness and the robustness of the proposed controller.     

Predictive modeling of human operators using parametric and neuro-fuzzy models by means of computer-based identification experiment

Driving a car and piloting an airplane are the most common examples for manual control of complicated processes. Human operators are known to be nonlinear, adaptive, time varying and intelligent controllers. In some cases, the human operator may or may not be well trained or an expert, showing different dynamics from operator to operator as in driving example. Therefore, it is very difficult to obtain mathematical models of human operators in a human-in-the-loop-manual control tasks. The goal of this research is to find a simple dynamic model for the prediction of the human operator actions in a manual control system. A computer-based experiment has been designed using the system identification theory to collect data from human operators. The autoregressive with exogenous inputs (ARX), as a parametric model and the adaptive-network-based fuzzy inference system (ANFIS), as an intelligent modeling approach that has the advantages of both neural networks and fuzzy logic, have been investigated and compared for simple and fast implementation to predict the response of human operators. ANFIS, having only 32 rules, provided much better prediction results than ARX model.     

Friction compensation of double inverted pendulum on a cart using locally linear neuro-fuzzy model

Double inverted pendulum on a cart (DIPC) is a highly nonlinear system. Due to its complex dynamics, it is widely used as a test-bed plant for the verification of newly designed controllers. In DIPC, two pendulums are kept upward by linear movements of cart. Because of this linear motions and frequent switching of velocity directions, another nonlinearity caused by friction becomes dominant around the equilibrium point. Friction introduces limit cycles to the system and results in a poor steady-state response. To eliminate these negative effects, the locally linear neuro-fuzzy (LLNF) approach is used to build an inverse model for friction compensation. This model is compared with multilayer perceptron network in order to demonstrate the better performance of LLNF. To stabilize DIPC, a common optimal controller is used, and despite its limited performance, experimental results show that the application of inverse modeling for friction compensation improves the steady-state response outstandingly.     

Robust adaptive controller based on evolving linear model applied to a Ball-Handling mechanism

The increasing complexity and permanent growth of real-world robotics formidable challenges demand that most control systems be intelligently adaptive to the parameters and structures of dynamics. This paper, therefore, discusses an extended sliding mode controller that is based on an evolving linear model (ELM) designed and implemented as a systematic approach to tackling the arms target angle tracking problem in the ball-handling system of a robot. Without any prior knowledge about the dynamics of the system other than its highest possible order, the dynamic orders and relative degrees of the system are practically derived. A novel online linearization technique based on the recursive least squares (RLS) method which keeps the output error of estimation in a relatively small bound is applied to identify the plant and to derive an adaptive-linear-regression (ALR) model of the system. Subsequently, having a model in which the number of constructing independent regressors varies over time, an extended sliding mode control strategy, established upon Lyapunov theory, is applied to the online-identifying ELM of the ball-handling system. In order to quantify the effectiveness of the proposed methodology, comparative analysis of the proposed strategy with well-established linear quadratic regulator (LQR) design and other suggested work on this topic, on the robustness of controllers, are performed in simulations. Ultimately, multifarious practical scenarios were designed, performed, and validated for the handling mechanism. The results clearly demonstrate the benefits and effectiveness of the design approaches in practice.     

Portfolio model for analyzing human resources: An approach based on neuro-fuzzy modeling and the simulated annealing algorithm

This paper presents a new model for developing a human resources portfolio based on a neuro-fuzzy approach. The adaptive neural network is constructed based on the Boston Consulting Group (BCG) portfolio matrix. The adaptive neural network was established by applying the simulated annealing algorithm. The model enables decision makers to evaluate and assess human resources potential in accordance with the environment and its circumstances. The purpose of creating this model is to enable insight into the existing potential and plan assets to improve and promote the employees’ potential in a company. The model allows the priorities of the suggested strategies to be defined, which eliminates one of the flaws of the classic BCG portfolio matrix. In this neuro-fuzzy model the input variables are described using fuzzy sets that are represented by Gaussian functions. Using expert reasoning a unique knowledge base is formed which enables employees to be scheduled by strategies. The portfolio model is tested in a realistic industrial environment.     

Dual approaches to strictly positive real controller synthesis with a performance using linear matrix inequalities

The synthesis of controllers that minimize a performance index subject to a strictly positive real (SPR) constraint is considered. Two controller synthesis methods are presented that are then combined into an iterative algorithm. Each method synthesizes optimal SPR controllers by posing a convex optimization problem where constraints are enforced via linear matrix inequalities. Additionally, each method fixes the controller state‐feedback gain matrix and finds an observer gain matrix such that an upper bound on the closed‐loop ‐norm is minimized and the controller is SPR. The first method retools the standard ‐optimal control problem by using a common Lyapunov matrix variable to satisfy both the criteria and the SPR constraint. The second method overcomes bilinear matrix inequality issues associated with the performance and the SPR constraint by employing a completing the square method and an overbounding technique. Both synthesis methods are used within an iterative scheme to find optimal SPR controllers in a sequential manner. Comparison of our synthesis methods to existing methods in the literature is presented. Copyright © 2012 John Wiley & Sons, Ltd.     

Process modeling and optimization of sorrel biodiesel synthesis using barium hydroxide as a base heterogeneous catalyst: appraisal of response surface methodology,neural network and neuro-fuzzy system

Neural Computing and Applications - In this study, three different modeling tools, viz. response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system...     

Virtual network embedding: ensuring correctness and optimality by construction using model transformation and integer linear programming techniques

Software and Systems Modeling - Virtualization technology allows service providers to operate data centers in a cost-effective and scalable manner. The data center network (substrate network) and...     

Control and synchronization of chaotic systems using a novel indirect model reference fuzzy controller

This paper presents a robust indirect model reference fuzzy control scheme for control and synchronization of chaotic nonlinear systems subject to uncertainties and external disturbances. The chaotic system with disturbance is modeled as a Takagi–Sugeno fuzzy system. Using a Lyapunov function, stable adaptation laws for the estimation of the parameters of the Takagi–Sugeno fuzzy model are derived as well as what the control signal should be to compensate for the uncertainties. The synchronization of chaotic systems is also considered in the paper. It is shown that by the use of an appropriate reference signal, it is possible to make the reference model follow the master chaotic system. Then, using the proposed model reference fuzzy controller, it is possible to force the slave to act as the reference system. In this way, the chaotic master and the slave systems are synchronized. It is shown that not only can the initial values of the master and the slave be different, but also there can be parametric differences between them. The proposed control scheme is simulated on the control and the synchronization of Duffing oscillators and Genesio–Tesi systems.     

Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, locally linear neuro-fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 min prediction horizon. The other two models are presented for the two faulty situations in the kiln with 7 min prediction horizon. At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used in this study.     

Frequency-domain identification of linear systems using arbitraryexcitations and a nonparametric noise model

Presents a generalized frequency domain identification method to identify single-input/single-output (SISO) systems combining two previously published extensions in one method: arbitrary but persistent excitations are allowed and a nonparametric noise model is extracted from the same data that are used to identify the system. The method is directly applicable to identification in feedback if an external persistently exciting reference signal is available     

Edge detection and linear feature extraction using a 2-D randomfield model

The edge-detection problem is posed as one of detecting step discontinuities in the observed correlated image, using directional derivatives estimated with a random field model. Specifically, the method consists of representing the pixels in a local window by a 2-D causal autoregressive (AR) model, whose parameters are adaptively estimated using a recursive least-squares algorithm. The directional derivatives are functions of parameter estimates. An edge is detected if the second derivative in the direction of the estimated maximum gradient is negatively sloped and the first directional derivative and a local estimate of variance satisfy some conditions. Because the ordered edge detector may not detect edges of all orientations well, the image scanned in four different directions, and the union of the four edge images is taken as the final output. The performance of the edge detector is illustrated using synthetic and real images. Comparisons to other edge detectors are given. A linear feature extractor that operates on the edges produced by the AR model is presented     

Analysis of a linear walking worker line using a combination of computer simulation and mathematical modeling approaches

It has become increasingly important in the last few years to develop rapid, dynamic, responsive and reconfigurable manufacturing processes and systems. This is because manufacturing enterprises are now being forced to develop and constantly improve their production systems so that they can quickly and economically react to unpredictable conditions such as varying production volumes and product variants with small lot size, high quality and low costs. One effective method to achieve this is to create a more flexible, highly skilled and agile workforce capable to perform multiple or all the required tasks in a production area where the system can be reconfigured easily as needed to accommodate changes of production requirement on a daily or weekly basis.This paper presents a study of a so-called linear walking worker assembly line based on a combination of computer simulation and mathematical analysis. The linear walking worker assembly line is a flexible assembly system where each worker travels down the line carrying out each assembly task at each station; and each worker accomplishes the assembly of a unit from start to finish. This design attempts to combine the flexibility of the U-shaped moving worker assembly cell with the efficiency of the conventional fixed worker assembly line. The paper aims to evaluate one critical factor of in-progress waiting time that affects the overall system performance providing a dynamic simulation outlook as well as an insight into the mechanism of such a flexible and reconfigurable manufacturing system.     

Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques

During the last three decades, the large spatial coverage of remote sensing data has been used in coral reef research to map dominant substrate types, geomorphologic zones, and bathymetry. During the same period, field studies have documented statistical relationships between variables quantifying aspects of the reef habitat and its fish community. Although the results of these studies are ambiguous, some habitat variables have frequently been found to correlate with one or more aspects of the fish community. Several of these habitat variables, including depth, the structural complexity of the substrate, and live coral cover, are possible to estimate with remote sensing data. In this study, we combine a set of statistical and machine-learning models with habitat variables derived from IKONOS data to produce spatially explicit predictions of the species richness, biomass, and diversity of the fish community around two reefs in Zanzibar. In the process, we assess the ability of IKONOS imagery to estimate live coral cover, structural complexity and habitat diversity, and we explore the importance of habitat variables, at a range of spatial scales, in the predictive models using a permutation-based technique. Our findings indicate that structural complexity at a fine spatial scale (∼ 5 to 10 m) is the most important habitat variable in predictive models of fish species richness and diversity, whereas other variables such as depth, habitat diversity, and structural complexity at coarser spatial scales contribute to predictions of biomass. In addition, our results demonstrate that complex model types such as tree-based ensemble techniques provide superior predictive performance compared to the more frequently used linear models, achieving a reduction of the cross-validated root-mean-squared prediction error of 3-11%. Although aerial photographs and airborne lidar instruments have recently been used to produce spatially explicit predictions of reef fish community variables, our study illustrates the possibility of doing so with satellite data. The ability to use satellite data may bring the cost of creating such maps within the reach of both spatial ecology researchers and the wide range of organizations involved in marine spatial planning.     

Three solutions for a robot arm controller using Pascal-Plus,occam and edison

Three currently available concurrent language systems, Pascal-Plus, occam and Edison, are used to implement a controller for a robot arm. The robot arm allows real parallelism of operation within the movements of the arm. The feasibility and restrictions placed upon the resultant solution for each of the language systems is then analysed and discussed. A Petri-net solution is also presented for the generalized problem and it is shown that each of the solutions is a different folding of the general net.     

Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR)

Pattern Analysis and Applications - In this paper, a new algorithm is proposed for fault detection and identification (FDI) in a class of nonlinear systems by combining the extended Kalman filter...     

Application of random forest,generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Spatial distribution of sponge species richness (SSR) and its relationship with environment are important for marine ecosystem management, but they are either unavailable or unknown. Hence we applied random forest (RF), generalised linear model (GLM) and their hybrid methods with geostatistical techniques to SSR data by addressing relevant issues with variable selection and model selection. It was found that: 1) of five variable selection methods, one is suitable for selecting optimal RF predictive models; 2) traditional model selection methods are unsuitable for identifying GLM predictive models and joint application of RF and AIC can select accuracy-improved models; 3) highly correlated predictors may improve RF predictive accuracy; 4) hybrid methods for RF can accurately predict count data; and 5) effects of model averaging are method-dependent. This study depicted the non-linear relationships of SSR and predictors, generated spatial distribution of SSR with high accuracy and revealed the association of high SSR with hard seabed features.     

Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need some centroid for the cluster initialization. Furthermore, the other researcher tried to apply ontology for the cluster on social network domain. This paper tries to reveal the behavior of students from all activities in Moodle e-learning system by putting ontology on domain social learning network (Moodle) which is not explored in the prior study. The activities such as forum, quiz, assignment, etc. are placed as clustering parameter according to the ontology model. The ontology of Moodle e-learning system is created to capture the activities of the student inside Moodle e-learning. Five meaningful attributes are used as group cluster for classifying the students' behaviour. According to the result, most of the students belong to the intentional group while some of the students belong to the constructive and active group. The constructed cluster is calculated based on the e-learning hits during the learning process inside Moodle e-learning. The result on the classification of students' behaviour using ontology cluster is comparable to their final achievement grade. It is believed that this study can bring immense benefit to the development of e-learning system in the future.