Fuzzy Adaptive Learning Research Articles

Coordination control of uncertain topological high-order multi-agent systems: distributed fuzzy adaptive iterative learning approach

This paper demonstrates that the method of T–S fuzzy model can be used to describe the uncertain topological structure for high-order linearly parameterized multi-agent systems (MAS). The dynamic of the leader is only available to a portion of the follower agents; thus, we present a novel distributed adaptive iterative learning control (AILC) protocol without using any global information to deal with the consensus problem of MAS under initial-state learning condition. It is proved that the proposed control protocol ensures all the internal signals in the multi-agent system are bounded, and the follower agents track the leader exactly on the finite time interval [0, T]; a sufficient condition is obtained for the exactly consensus result of the multi-agent system by choosing the appropriate composite energy function. Extensions to the formation control of multi-agent systems are also given. In the end, illustrative examples are shown to verify the availability of the proposed AILC scheme.

Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

ABSTRACTIn this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T–S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

CONCEPTUAL COST ESTIMATIONS USING NEURO-FUZZY AND MULTI-FACTOR EVALUATION METHODS FOR BUILDING PROJECTS

During the conceptual phase of a construction project, numerous uncertainties make accurate cost estimation challenging. This work develops a new model to calculate conceptual costs of building projects for effective cost control. The proposed model integrates four mathematical techniques (sub-models), namely, (1) the component ratios sub-model, (2) fuzzy adaptive learning control network (FALCON) and fast messy genetic algorithm (fmGA) based sub-model, (3) regression sub-model, and (4) multi-factor evaluation sub-model. While the FALCON- and fmGA-based sub-model trains the historical cost data, three other sub-models assess the inputs systematically to estimate the cost of a new pro­ject. This study also closely examines the behavior of the proposed model by evaluating two modified models without considering fmGA and undertaking sensitivity analysis. Evaluation results indicate that, with the ability to more thor­oughly respond to the project characteristics, the proposed model has a high probability of increasing estimation accura­cies more than the three conventional methods, i.e., average unit cost, component ratios, and linear regression methods.

Fuzzy Pi Adaptive Learning Controller for Controlling the Angle of Attack of an Aircraft

In this paper, a Fuzzy PI Adaptive Learning controller is proposed for a flight control system to control the angle of attack of an aircraft. The proposed controller tracks the reference angle as desired by the pilot of the aircraft. The performance indices are evaluated and the corresponding value is compared with that for the conventional controllers obtained from Zigler Nichols (ZN), Tyreus Luyben (TL) and Extended Skogestad Internal Model Controller (ESIMC). The performance indices such as Mean Square Error (MSE), Integral Absolute Error (IAE) and Integral Absolute Time Error (IATE) are evaluated to verify superiority of one over another.

Multi-state systems reliability with composite importance measures of fuzzy petri nets

A fuzzy petri nets method for modelling and analysing the reliability of a multi-state software system is proposed here. This new method is based on fuzzy parameter of the FPN, which depends on the state probability which is learned by the enhanced fuzzy reasoning algorithm. First, the multi-state system model of fuzzy petri nets was proposed. Second, fuzzy petri nets adaptive learning algorithms were proposed, and the state probability is drawn to the optimal value. Then the reliability of the multi-state system was determined. Finally, with respect to the different Type 1 importance measures, according to the state probability, multi-state system, the reliability of the state with fuzzy petri nets (FPN) composite importance measures (CIM) was estimated and the comparison results table between the CIM and the FPN CIM was produced. From the experiment, multi-state system reliability with composite importance measures of fuzzy petri nets in this paper shows that the variances are consistently low, and the measures can be considered to be robust. This makes the system more reliable and accuracy from the user's perspective, the method becomes more efficient.

Multi-state systems reliability with composite importance measures of fuzzy petri nets

A fuzzy petri nets method for modelling and analysing the reliability of a multi-state software system is proposed here. This new method is based on fuzzy parameter of the FPN, which depends on the state probability which is learned by the enhanced fuzzy reasoning algorithm. First, the multi-state system model of fuzzy petri nets was proposed. Second, fuzzy petri nets adaptive learning algorithms were proposed, and the state probability is drawn to the optimal value. Then the reliability of the multi-state system was determined. Finally, with respect to the different Type 1 importance measures, according to the state probability, multi-state system, the reliability of the state with fuzzy petri nets (FPN) composite importance measures (CIM) was estimated and the comparison results table between the CIM and the FPN CIM was produced. From the experiment, multi-state system reliability with composite importance measures of fuzzy petri nets in this paper shows that the variances are consistently low, and the measures can be considered to be robust. This makes the system more reliable and accuracy from the user's perspective, the method becomes more efficient.

An efficient adaptive fuzzy learning diagnosis method for e-Learning

Most e-Learning systems are required to establish a flexible content structure and provide suitable learning path, content, or interface by extracting meaningful learning behavior of students for adaptive learning. However, recognizing the students' learning behavior, teaching material, and personal degree are still a challenge that needs to be resolved. In this paper, we propose an efficient fuzzy algorithm using in similarity measurement for selecting suitable content and exams for students, which has been applied to the e-Learning system called Fuzzy Adaptive Learning Diagnosis System (FALDS). The proposed system computes the relationship between exam items and teaching materials depending on the results of practice and then exams to automatically select and marks important paragraphs for the learners. In addition, an efficient system for classifying students into groups so that information for selecting appropriate items for the learners can be provided is proposed. To evaluate the performance, a total of 200 fourth-grade students from six classes participate in the experiment for a school term. The results indicate that by using FALDS to diagnose and assist learning, students in the experimental group outperform those not in the group.

Neuro‐Fuzzy Cost Estimation Model Enhanced by Fast Messy Genetic Algorithms for Semiconductor Hookup Construction

Abstract: Semiconductor hookup construction (i.e., constructing process tool piping systems) is critical to semiconductor fabrication plant completion. During the conceptual project phase, it is difficult to conduct an accurate cost estimate due to the great amount of uncertain cost items. This study proposes a new model for estimating semiconductor hookup construction project costs. The developed model, called FALCON‐COST, integrates the component ratios method, fuzzy adaptive learning control network (FALCON), fast messy genetic algorithm (fmGA), and three‐point cost estimation method to systematically deal with a cost‐estimating environment involving limited and uncertain data. In addition, the proposed model improves the current FALCON by devising a new algorithm to conduct building block selection and random gene deletion so that fmGA operations can be implemented in FALCON. The results of 54 case studies demonstrate that the proposed model has estimation accuracy of 83.82%, meaning it is approximately 22.74%, 23.08%, and 21.95% more accurate than the conventional average cost method, component ratios method, and modified FALCON‐COST method, respectively. Providing project managers with reliable cost estimates is essential for effectively controlling project costs.

SynRM 드라이브의 고성능 제어를 위한 RFNN 제어기 설계

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

유도전동기 드라이브의 DTC를 위한 하이브리드 퍼지제어기

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

AFLC를 이용한 IPMSM 드라이브의 NN 파라미터 추정

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance and adaptive fuzzy learning contrroller(AFLC) for speed control in IPMSM Drives. AFLC is chaged fuzzy rule base by rule base modifier for robust control of IPMSM. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator and AFLC is confirmed by comparing to conventional algorithm.

다중 AFLC를 이용한 SynRM 드라이브의 효율 최적화 제어

SynRM 효율최적화 제어는 다른 교류전동기에 비해 SynRM의 효율이 낮기 때문에 에너지 절약과 환경보존의 관점에서 매우 중요하다. 본 논문에서는 다중 AFLC를 이용하여 철손을 고려한 SynRM의 새로운 효율 최적화 제어를 제안하였다. 최대효율에서 SynRM을 구동하기 위해 토크전류와 여자전류사이의 최적 전류비를 분석하여 구한다.BR 본 논문에서는 동손과 철손을 최소로 하는 SynRM의 효율 최적화 제어를 제안하였다. 특정한 모터토크를 제공하는 d 축과 q 축 전류의 다양한 조합이 존재한다. 효율 최적화의 목적은 정상상태에서 최소 손실을 제공하는 d 축과 q 축 전류의 조합을 찾는 것이며, 제안된 제어기의 제어 성능은 다양한 동작조건의 분석을 통해 평가되었다. 분석된 결과는 제안된 알고리즘의 타당성을 입증한다.

Efficiency Optimization Control of IPMSM Drive using Multi AFLC

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. This paper proposes efficiency optimization control of IPMSM drive using adaptive fuzzy learning controller(AFLC). In order to optimize the efficiency the loss minimization algorithm is developed based on motor model and operating condition. The d-axis armature current is utilized to minimize the losses of the IPMSM in a closed loop vector control environment. The design of the current based on adaptive fuzzy control using model reference and the estimation of the speed based on neural network using ANN controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the optimal control of the armature current. The minimization of loss is possible to realize efficiency optimization control for the proposed IPMSM. The optimal current can be decided according to the operating speed and the load conditions. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AFLC. Also, this paper proposes speed control of IPMSM using AFLC1, current control of AFLC2 and AFLC3, and estimation of speed using ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled AFLC, the operating characteristics controlled by efficiency optimization control are examined in detail.

A novel brain-inspired neural cognitive approach to SARS thermal image analysis

Thermal imaging is used extensively in the detection of infrared spectrum. This principle has found great and effective use in the screening of potential SARS patients. This paper investigates the application of several novel brain-inspired softcomputing techniques in the study of the correlation of superficial thermal images against the true internal body temperature. Given some backgrounds that the existing infrared systems used at various boarder checkpoints have high false-negative rate, the novel fuzzy neural networks (FNNs) employed in the back-end of the system have a role as a thermal analysis tool with high degree of accuracy. To achieve the automation and improve the accuracy in the feature extraction process of the infrared images, some forms of image processing technique based on the novel FNNs are also proposed in this paper. Extensive experimentations are undertaken to examine such intelligent medical decision support tool. Benchmarking was carried out on the novel FNN architectures which include pseudo outer-product fuzzy neural network (POPFNN), evolving fuzzy neural network (EFuNN), fuzzy adaptive learning control network (Falcon), generic self-organizing fuzzy neural network (GenSoFNN), and fuzzy cerebellar model articulation controller (FCMAC), and their results are promising. The performance of the GenSoFNN network is the most appealing among others. In addition, the experiments are conducted on real-life data taken from the Emergency Department (A&E), Tan Tock Seng Hospital (the designated SARS center in Singapore) to confirm the validity of such design in the real time.

A novel neuro-cognitive approach to modeling traffic control and flow based on fuzzy neural techniques

In many developed and developing countries, efficient monitoring and controlling of the city’s traffic have become major challenges. Conventional traffic light control methods, Preset Cycle Timing and Preset Cycle Timing with proximity sensors used today are neither sufficiently efficient nor effective to manage different traffic conditions. One solution is to employ a human operator. Unfortunately this method is expensive and error-prone due to lapse in concentration and other factors. Another alternative is to introduce an intelligent controller using fuzzy neural learning memory techniques, which have the capability to mimic human intelligence in controlling the frequency of traffic light changes at a junction. The performances of four suitable soft-computing architectures are investigated in this study as a possible platform to model and develop an intelligent traffic light control regime. These neural fuzzy learning structures construct memories that possess the intelligence and capabilities of a human operator in monitoring and managing the traffic at road intersections under different traffic scenarios. An open source traffic light simulator, Green Light District, is used to create and simulate different traffic conditions at (i) a simple traffic light intersection and (ii) a complex traffic light intersection. Traffic data generated by the simulator under the control of a human operator is then used as inputs for the training and testing of four fuzzy neural network architectures. The four architectures are Generic Self-organizing Fuzzy Neural Network (GenSoFNN), Pseudo Outer Product based Fuzzy Neural Network (POPFNN), Fuzzy Adaptive Learning Control Network (Falcon) and Multilayer Perceptrons (MLP). The performance of each of the neural network architectures was found to be promising from the simulation results derived for both simple and complex traffic light intersections. Performance was based on the mean classified rate, mean training time, mean number of rules, and standard deviation of the classified rate across the traffic conditions simulated. A technique from each of the architectures with the best results is subsequently selected for more in-depth study on its performance in a complex traffic light intersection. Although all the selected techniques from the four architectures suffered a decline in performance in the complex traffic light intersection; architectures such as GenSoFNN and Falcon continue to produce good results. The POPFNN architecture generated a large number of rules and the MLP architecture produced poor classified rates. This work has demonstrated that it is highly feasible to develop neuro-cognitive traffic control regime that can mimic the behaviors of a human operator.

Ovarian cancer diagnosis with complementary learning fuzzy neural network

Early detection is paramount to reduce the high death rate of ovarian cancer. Unfortunately, current detection tool is not sensitive. New techniques such as deoxyribonucleic acid (DNA) micro-array and proteomics data are difficult to analyze due to high dimensionality, whereas conventional methods such as blood test are neither sensitive nor specific. Thus, a functional model of human pattern recognition known as complementary learning fuzzy neural network (CLFNN) is proposed to aid existing diagnosis methods. In contrast to conventional computational intelligence methods, CLFNN exploits the lateral inhibition between positive and negative samples. Moreover, it is equipped with autonomous rule generation facility. An example named fuzzy adaptive learning control network with another adaptive resonance theory (FALCON-AART) is used to illustrate the performance of CLFNN. The confluence of CLFNN-micro-array, CLFNN-blood test, and CLFNN-proteomics demonstrate good sensitivity and specificity in the experiments. The diagnosis decision is accurate and consistent. CLFNN also outperforms most of the conventional methods. This research work demonstrates that the confluence of CLFNN-DNA micro-array, CLFNN-blood tests, and CLFNN-proteomic test improves the diagnosis accuracy with higher consistency. CLFNN exhibits good performance in ovarian cancer diagnosis in general. Thus, CLFNN is a promising tool for clinical decision support.

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.

Reinforcement learning combined with a fuzzy adaptive learning control network (FALCON-R) for pattern classification

Reinforcement learning has been widely-used for applications in planning, control, and decision making. Rather than using instructive feedback as in supervised learning, reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern classification problem as a reinforcement learning problem. The problem is realized with a temporal difference method in a FALCON-R network. FALCON-R is constructed by integrating two basic FALCON-ART networks as function approximators, where one acts as a critic network (fuzzy predictor) and the other as an action network (fuzzy controller). This paper serves as a guideline in formulating a classification problem as a reinforcement learning problem using FALCON-R. The strengths of applying the reinforcement learning method to the pattern classification application are demonstrated. We show that such a system can converge faster, is able to escape from local minima, and has excellent disturbance rejection capability.

Fuzzy System-Based Adaptive Iterative Learning Control for Nonlinear Plants With Initial State Errors

In this paper, a fuzzy system-based adaptive iterative learning controller is proposed for a class of non-Lipschitz nonlinear plants which can repeat a given task over a finite time interval. The variable initial resetting state errors at the beginning of each trial is considered. To overcome the initial errors, a time-varying boundary layer is introduced to design an error function. Based on the error function, the main structure of this controller is constructed by a fuzzy iterative learning component and a feedback stabilization component. The fuzzy system is used as an approximator to compensate for the plant unknown nonlinearity. Since the optimal parameters for a good fuzzy approximation are in general unavailable, the adaptive algorithms are derived along the iteration axis to search for suitable parameter values and then guarantee the closed-loop stability and learning convergence. It is shown that all the adjustable parameters as well as internal signals remain bounded for all iterations. There even exist initial state errors, the norm of tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity and the learning speed can be easily improved if the learning gain is large.

Model reference fuzzy adaptive learning system

Fuzzy control has revealed as a practical alternative to several conventional control schemes since it has shown good results in some application areas. However, there are several drawbacks of this approach: (i) the design of fuzzy controllers is usually performed in an ad hoc manner where it is often difficult to choose some of the controller parameters (e.g., the membership functions), and (ii) the fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur.A “learning system” possesses the capability to improve its performance over time by interacting with its environment. A learning control system is designed so that its “learning controller” has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. Learning controllers are often designed to mimic the manner in which a human in the control loop would learn how to control a system while it operates. Some characteristics of this human learning process my include: (i) after learning how to control the plant for some operating condition, if the operating conditions change, then the best way to control the system may have to be relearned; (ii) a human with significant amount of experience at controlling the system in one operating region should not forget this experience if the operating condition changes. To mimic these types of human learning behavior, we introduce strategy that can be used to learning controller onto the current operating region of the system.