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At least 829 records · Page 46Linked to original sources
PubMed2001

Comparative study of a learning fuzzy PID controller and a self-tuning controller.

The self-organising fuzzy controller is an extension of the rule-based fuzzy controller with an additional learning capability. The self-organising fuzzy (SOF) is used as a master controller to readjust conventional PID gains at the actuator level during the system operation, copying the experience of a human operator. The application of the self-organising fuzzy PID (SOF-PID) controller to a 2-link non-linear revolute-joint robot-arm is studied using path tracking trajectories at the setpoint. For the purpose of comparison, the same experiments are repeated by using the self-tuning controller subject to the same data supplied at the setpoint. For the path tracking experiments, the output trajectories of the SOF-PID controller followed the specified path closer and smoother than the self-tuning controller.

Algorithms↗
PubMed2002

A study of control strategy for the bin system with tube mill in the coal fired power station.

The pulverized coal systems with tube mills are widely used in coal fired power plants. As there are many controlled variables and disturbances in this system, and startup and shutdown of the system is frequent and the dynamic characteristic of the controlled plant is also extraordinary, the automatic control system for this kind of pulverized coal system is not ideally implemented for long. The authors of this paper investigated and tested a pulverized coal system with tube mill in Nanjing Thermal Power Station, and based on various different controlled plants and control objectives, proposed rather independent control subsystems. These subsystems can organize a whole automatic control system of pulverized coal system that can be used in different conditions. The control system presented in this paper has worked well since it was put into operation in May 1998, and good economic benefits have been acquired.

Algorithms↗
PubMed2002

Sensor fusion by pseudo information measure: a mobile robot application.

In any autonomous mobile robot, one of the most important issues to be designed and implemented is environment perception. In this paper, a new approach is formulated in order to perform sensory data integration for generation of an occupancy grid map of the environment. This method is an extended version of the Bayesian fusion method for independent sources of information. The performance of the proposed method of fusion and its sensitivity are discussed. Map building simulation for a cylindrical robot with eight ultrasonic sensors and mapping implementation for a Khepera robot have been separately tried in simulation and experimental works. A new neural structure is introduced for conversion of proximity data that are given by Khepera IR sensors to occupancy probabilities. Path planning experiments have also been applied to the resulting maps. For each map, two factors are considered and calculated: the fitness and the augmented occupancy of the map with respect to the ideal map. The length and the least distance to obstacles were the other two factors that were calculated for the routes that are resulted by path planning experiments. Experimental and simulation results show that by using the new fusion formulas, more informative maps of the environment are obtained. By these maps more appropriate routes could be achieved. Actually, there is a tradeoff between the length of the resulting routes and their safety and by choosing the proper fusion function, this tradeoff is suitably tuned for different map building applications.

Algorithms↗
PubMed2002

Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system.

The human sensory test is often used for obtaining the sensory quantities of odors, however, the fluctuation of results due to the expert's condition can cause discrepancies among panelists. Authors have studied the artificial odor discrimination system using a quartz resonator sensor and a back-propagation neural network as the recognition system, however, the unknown category of odor is always recognized as the known category of odor. In this paper, a kind of fuzzy algorithm for learning vector quantization (LVQ) is developed and used as a pattern classifier. In this type of fuzzy LVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistics of the measurement error directly. During learning, the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated by shifting the central position of the fuzzy reference vector toward or away from the input vector, and by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of fuzzy-neuro (FN) LVQ is different in nature from fuzzy algorithm (FA) LVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in an artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ could provide high recognition probability in determining various known categories of odors, however, the FNLVQ neural system has the ability to recognize the unknown category of odor that could not be recognized by the FALVQ neural system.

Algorithms↗
PubMed2002

Fuzzy control of multivariable process by modified error decoupling.

In this paper, a control concept for the squared (equal number of inputs and outputs) multivariable process systems is given. The proposed control system consists of two parts, single loop fuzzy controllers in each loop and a centralized decoupling unit. The fuzzy control system uses feedback control to minimize the error in the loop and the decoupler uses an adaptive technique to mitigate loop interactions. The decoupler predicts the interacting loop changes and modifies the input (error) of the loop controller. The controller was tested on the simulation model of "single component vaporizer" process. The results indicate that the decoupling controller shows better performance for set point and load changes.

Algorithms↗
PubMed2002

Supervisory control design based on hybrid systems and fuzzy events detection. Application to an oxichlorination reactor.

This paper presents a supervisory control scheme based on hybrid systems theory and fuzzy events detection. The fuzzy event detector is a linguistic model, which synthesizes complex relations between process variables and process events incorporating experts' knowledge about the process operation. This kind of detection allows the anticipation of appropriate control actions, which depend upon the selected membership functions used to characterize the process under scrutiny. The proposed supervisory control scheme was successfully implemented for an oxichlorination reactor in a vinyl monomer plant. This implementation has allowed improvement of reactor stability and reduction of raw material consumption.

Computer Simulation↗
PubMed2003

Digital redesign of anti-wind-up controller for cascaded analog system.

The cascaded conventional anti-wind-up (CAW) design method for integral controller is discussed. Then, the prediction-based digital redesign methodology is utilized to find the new pulse amplitude modulated (PAM) digital controller for effective digital control of the analog plant with input saturation constraint. The desired digital controller is determined from existing or pre-designed CAW analog controller. The proposed method provides a novel methodology for indirect digital design of a continuous-time unity output-feedback system with a cascaded analog controller as in the case of PID controllers for industrial control processes with the presence of actuator saturations. It enables us to implement an existing or pre-designed cascaded CAW analog controller via a digital controller effectively.

Equipment Design↗
PubMed2003

Diagnosis of the industrial systems by fuzzy classification.

The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parametrization method which is not necessarily based on an iterative training. This approach can be seen as a pre-parametrization which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a dc motor showing the effectiveness of this method.

Algorithms↗
PubMed2003

Calculation of PID controller parameters by using a fuzzy neural network.

In this paper, we use the fuzzy neural network (FNN) to develop a formula for designing the proportional-integral-derivative (PID) controller. This PID controller satisfies the criteria of minimum integrated absolute error (IAE) and maximum of sensitivity (Ms). The FNN system is used to identify the relationship between plant model and controller parameters based on IAE and Ms. To derive the tuning rule, the dominant pole assignment method is applied to simplify our optimization processes. Therefore, the FNN system is used to automatically tune the PID controller for different system parameters so that neither theoretical methods nor numerical methods need be used. Moreover, the FNN-based formula can modify the controller to meet our specification when the system model changes. A simulation result for applying to the motor position control problem is given to demonstrate the effectiveness of our approach.

Algorithms↗
PubMed1999

Nonlinear control of an activated sludge aeration process: use of fuzzy techniques for tuning PID controllers.

In this paper, several tuning algorithms, specifically ITAE, IMC and Cohen and Coon, were applied in order to tune an activated sludge aeration PID controller. Performance results of these controllers were compared by simulation with those obtained by using a nonlinear fuzzy PID controller. In order to design this controller, a trial and error procedure was used to determine, as a function of error at current time and at a previous time, sets of parameters (including controller gain, integral time and derivative time) which achieve satisfactory response of a PID controller actuating over the aeration process. Once these sets of data were obtained, neural networks were used to obtain fuzzy membership functions and fuzzy rules of the fuzzy PID controller.

Algorithms↗
PubMed1998

Multiple correspondence analysis of biomechanical signals characterized through fuzzy histograms.

Data characterizing is considered the first and main stage of the statistical analysis. Rather than characterizing each biomechanical signal through one or few global indicators, such as the mean or the root mean square, this paper suggests first to cut the scale into several fuzzy windows and to summarize the data within each window through an occurrence indicator. These indicators become the analysis variables. They can be analyzed through the multiple correspondence analysis, which shows the most discriminant variables, connections between them, empirical situation classes and correspondences between these classes and the most discriminant variables. An example is considered for arguing our point of view; it concerns characterizing and analysis of forces situated at the hand, foot and back level in a load lifting task.

Adult↗
PubMed1994

Dipolar electrocardiotopographic evaluation of ventricular activation in patients with various degrees of coronary artery disease.

Dipolar electrocardiotopographic imaging of instantaneous vectorcardiographic QRS vectors represents the ventricular activation front as a bounded area on the spherical image surface. Fuzzy set treatment of the elements of this area allows for the recognition of five classes of ventricular activation: I, abnormal; II, abnormal with normal component; III, normal with abnormal component; IV, marginally normal; and V, normal. In a group of 71 patients with chest pain and various degrees of coronary artery stenosis classes II, III, and IV were most frequently observed in patients with one- or two-vessel disease. The frequency distributions of classes II and III throughout the QRS were bimodal with peaks at 20 and 60 ms after QRS onset, while the frequency distribution of class IV was unimodal with a peak at 30 ms after QRS onset. The advantage of this method is the smoothing of threshold values on a quantitative basis.

Adolescent↗
PubMed2000

Computer-aided detection and diagnosis of breast cancer.

The limitations of radiologists when interpreting mammogram examinations provides a reasonable, if not compelling, basis for application of computer techniques that have the potential to improve diagnostic performance. Computer algorithms, at their present state of development, show great promise for clinical use. It can be expected that such use will only improve as computer technology and computer methods continue to become more formidable. The eventual role of computers in mammographic detection and diagnosis has not been fully defined, but their effect on practice may one day be very significant.

Algorithms↗
PubMed2002

Diagnosing reservoir water quality using self-organizing maps and fuzzy theory.

Since trophic status assessment of water quality is very important for the water resources management, the assessment results obtained from using only one parameter may easily mislead or bias the decision makers or managers. Even when using a multivariable index, how to determine the weights of all factors is debatable. In this research, one complementary evaluation method, self-organizing map (SOM), for diagnosing water quality has been used to develop a trophic state classifier and is illustrated with a case study of trophic status assessment for Fei-Tsui Reservoir in Taiwan. The historical database was collected from the management agency of Fei-Tsui Reservoir from 1987 to 1995. The results of SOM are compared with those of the Carlson index and Fuzzy synthetic evaluation, showing that the inconsistent records can be mapped to the conflicting data zone of the SOM output map. In addition, SOM creates a diagnostic axis on the map to express the trophic status of the water body. As long as the SOM model is well-trained, new records can be assessed and classified as either one of three trophic levels or speciarcases. If special water quality conditions are expressed on the SOM output, those data can reveal that either total phosphorus (TP) or chlorophyll A (chl a) is higher than usual.

Chlorophyll↗
PubMed2002

Integrated assessment for aerobic biodegradability of organic substances.

In this paper, the amount of oxygen consumption, end products and activities of microorganisms are considered as the three factors which affect the biodegradability of organic substances in water. Two integrated assessment methods for biodegradability of organic substances, fuzzy clustering integrated assessment and weighted integrated assessment, have been developed. Simultaneously, the detail steps for assessing a new organic substance biodegradability with these two integrated assessment methods are proposed.

Bacteria, Aerobic↗
PubMed1998

An indicator of pesticide environmental impact based on a fuzzy expert system.

Pesticide use options available to farmers differ strongly with respect to the risks they pose to the environment. This paper proposes a fuzzy expert system to calculate an indicator "Ipest" which reflects an expert perception of the potential environmental impact of the application of a pesticide in a field crop. We defined four modules, one reflecting the presence (rate of application) of the pesticide, the other three reflecting the risk for three major environmental compartments (groundwater, surface water, air). The input variables for these modules are pesticide properties, site-specific conditions and characteristics of the pesticide application. For each input variable two functions describing membership to the fuzzy subsets Favourable (F) and Unfavourable (U) have been defined. These functions are based on criteria drawn from the literature or on the authors' expert judgment. The expert system calculates the value of modules according to the degree of membership of the input variables to the fuzzy subsets F and U and according to sets of decision rules. The four modules can be considered individually or can be aggregated (again according to membership to fuzzy subsets F and U and a set of decision rules) into the indicator Ipest. The system is flexible and can be turned to expert perception, it can be used as a decision aid tool to rank or choose between alternative pesticide application options with respect to their potential environmental impact. Results of a sensitivity analysis and module and Ipest scores for some pesticide application cases are presented. An agro-ecological indicator IPEST, based on the expert system, is proposed as a tool to assess the environmental impact of all pesticide applications related to a crop within a year. The practical implementation of the expert system and its validation are discussed.

Biodegradation, Environmental↗
PubMed2000

An algebraic/graphical tool to compare ecosystems with respect to their pollution V: cluster analysis and Hasse diagrams.

In case of large data matrices comparative evaluations of objects/regions with the technique of Hasse diagrams may be troublesome due to a messy system of lines in the graphical representation. Here fuzzy clustering leads to useful simplifications because regions with slightly different pollution pattern are grouped together. However, fuzzy clustering implies to introduce a threshold value for the membership of an object to a cluster and to select the best number of clusters. Therefore many arbitrarities evolve. Within the systematic study presented here we found that some objects are very stable against variations of the threshold value and the number of cluster whereas other objects behaves different. According to their behaviour we investigated a classification of the objects. Formal Concept Analysis shows that in some cases specific pollution pattern imply the membership to one of these classes. For example objects which are characterized by high Pb-, Zn-concentration and moderate S-concentration imply a high stability against variants of the clustering process. Further implications are described in the paper.

Cadmium↗
PubMed2000

Fuzzy clustering analysis of the first 10 MEIC chemicals.

In this paper, we discuss the classification results of the toxicological responses of 32 in vivo and in vitro test systems to the first 10 MEIC chemicals. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering and a new clustering technique, namely fuzzy hierarchical cross-classification. The characteristics clustering technique produces fuzzy partitions of the characteristics (chemicals) involved and thus it is a useful tool for studying the (dis)similarities between different chemicals and for essential chemicals selection. The cross-classification algorithm produces not only a fuzzy partition of the test systems analyzed, but also a fuzzy partition of the considered 10 MEIC (multicentre evaluation of in vitro cytotoxicity) chemicals. In this way it is possible to identify which chemicals are responsible for the similarities or differences observed between different groups of test systems. In another way, there is a specific sensitivity of a chemical for one or more toxicological tests.

Algorithms↗