A fuzzy self‐organizing map algorithm for biological pattern recognition

Data clustering is a key task for various processes including sequence analysis and pattern recognition. This paper studies a clustering algorithm that aimed to increase accuracy and sensitivity when working with biological data such as DNA sequences. The new algorithm is a modified version of fuzzy C‐means (FCM) and is based on the well‐known self‐organizing map (SOM). In order to show the performance of the algorithm, seven different data sets are processed. The experimental results demonstrate that the proposed algorithm has the potential to outperform SOM and FCM in terms of clustering and classification accuracy abilities. Additionally, a brief comparison is made the proposed algorithm with some previously studied ‘FCM‐SOM’ hybrid algorithms from the literature.     

A multi-objective evolutionary method for learning granularities based on fuzzy discretization to improve the accuracy-complexity trade-off of fuzzy rule-based classification systems: D-MOFARC algorithm

Multi-objective evolutionary algorithms represent an effective tool to improve the accuracy-interpretability trade-off of fuzzy rule-based classification systems. To this aim, a tuning process and a rule selection process can be combined to obtain a set of solutions with different trade-offs between the accuracy and the compactness of models. Nevertheless, an initial model needs to be defined, in particular the parameters that describe the partitions and the number of fuzzy sets of each variable (i.e. the granularities) must be determined. The simplest approach is to use a previously established single granularity and a uniform fuzzy partition for each variable. A better approach consists in automatically identifying from data the appropriate granularities and fuzzy partitions, since this usually leads to more accurate models.This contribution presents a fuzzy discretization approach, which is used to generate automatically promising granularities and their associated fuzzy partitions. This mechanism is integrated within a Multi-Objective Fuzzy Association Rule-Based Classification method, namely D-MOFARC, which concurrently performs a tuning and a rule selection process on an initial knowledge base. The aim is to obtain fuzzy rule-based classification systems with high classification performances, while preserving their complexity.     

MAGAD-BFS: A learning method for Beta fuzzy systems based on a multi-agent genetic algorithm

This paper proposes a learning method for Beta fuzzy systems (BFS) based on a multiagent genetic algorithm. This method, called Multi-Agent Genetic Algorithm for the Design of BFS has two advantages. First, thanks to genetic algorithms (GA) efficiency, it allows to design a suitable and precise model for BFS. Second, it improves the GA convergence by reducing rule complexity thanks to the distributed implementation by multi-agent approach. Dynamic agents interact to provide an optimal solution in order to obtain the best BFS reaching the balance interpretability-precision. The performance of the method is tested on a simulated example.     

A self‐tuning iterative learning controller for time variant systems

We consider the iterative learning control problem from an adaptive control viewpoint. The self‐tuning iterative learning control systems (STILCS) problem is formulated in a general case, where the underlying linear system is time‐variant and its parameters are all unknown and where its initial conditions are not constant and not determinable in various iterations. A procedure for solving this problem will be presented. The Lyapunov technique is employed to ensure the convergence of the presented STILCS. Computer simulation results are included to illustrate the effectiveness of the proposed STILCS. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Fault isolation in manufacturing systems based on learning algorithm and fuzzy rule selection

Neural Computing and Applications - This paper addresses the fault detection and isolation problem in manufacturing systems. Some of these systems can be affected by several faults, a first way of...     

The impact of team‐based learning on students with different self‐regulated learning abilities

Team‐based learning (TBL) stresses applying knowledge rather than absorbing knowledge in class; studies have investigated the use of TBL and its merits in different teaching courses (e.g., medical science and business). TBL is most effective when students learn autonomously before class. However, the ability of autonomous learning is highly associated with the ability of self‐regulated learning (SRL); most importantly, not every student possesses good (or high) SRL ability. Nevertheless, few studies have compared the effectiveness of TBL in students with different SRL abilities. To address this issue, this study analyzed approximately 90 students, whose course teaching involves office application software (Microsoft Excel). This study also developed an online TBL system (called Online TBL) to facilitate performing TBL and to collect the learning behaviours of students with different (high or low) SRL abilities on each TBL stage. The analytical results show that compared with the low‐SRL students, the high‐SRL students were more prepared for class because they spent more reviewing material and had better scores on personal uploaded Excel and Individual Readiness Assurance Test. From the feedback of the peer evaluation, the results also show that the high‐SRL students received more credits than the low‐SRL students did. The questionnaire survey revealed that both low‐SRL and high‐SRL students had a favourable impression of TBL. Further discussion is given to explain the above results.     

Dynamic output‐feedback fuzzy MPC for Takagi‐Sugeno fuzzy systems under event‐triggering–based try‐once‐discard protocol

The fuzzy model predictive control (FMPC) problem is studied for a class of discrete‐time Takagi‐Sugeno (T‐S) fuzzy systems with hard constraints. In order to improve the network utilization as well as reduce the transmission burden and avoid data collisions, a novel event‐triggering–based try‐once‐discard (TOD) protocol is developed for networks between sensors and the controller. Moreover, due to practical difficulties in obtaining measurements, the dynamic output‐feedback method is introduced to replace the traditional state feedback method for addressing the FMPC problem. Our aim is to design a series of controllers in the framework of dynamic output‐feedback FMPC for T‐S fuzzy systems so as to find a good balance between the system performance and the time efficiency. Considering nonlinearities in the context of the T‐S fuzzy model, a “min‐max” strategy is put forward to formulate an online optimization problem over the infinite‐time horizon. Then, in light of the Lyapunov‐like function approach that fully involves the properties of the T‐S fuzzy model and the proposed protocol, sufficient conditions are derived to guarantee the input‐to‐state stability of the underlying system. In order to handle the side effects of the proposed event‐triggering–based TOD protocol, its impacts are fully taken into consideration by virtue of the S‐procedure technique and the quadratic boundedness methodology. Furthermore, a certain upper bound of the objective is provided to construct an auxiliary online problem for the solvability, and the corresponding algorithm is given to find the desired controllers. Finally, two numerical examples are used to demonstrate the validity of proposed methods.     

Local identification of prototypes for genetic learning of accurate TSK fuzzy rule‐based systems

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics‐based Takagi–Sugeno–Kang (TSK) rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two‐stage evolutionary algorithm based on MOGUL (a methodology to obtain Genetic Fuzzy Rule‐Based Systems under the Iterative Rule Learning approach) has been developed to consider the interaction between input and output variables. The first stage performs a local identification of prototypes to obtain a set of initial local semantics‐based TSK rules, following the Iterative Rule Learning approach and based on an evolutionary generation process within MOGUL (taking as a base some initial linguistic fuzzy partitions). Because this generation method induces competition among the fuzzy rules, a postprocessing stage to improve the global system performance is needed. Two different processes are considered at this stage, a genetic niching‐based selection process to remove redundant rules and a genetic tuning process to refine the fuzzy model parameters. The proposal has been tested with two real‐world problems, achieving good results. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 909–941, 2007.     

MRMOGA: a new parallel multi‐objective evolutionary algorithm based on the use of multiple resolutions

In this paper, we introduce MRMOGA (Multiple Resolution Multi‐Objective Genetic Algorithm), a new parallel multi‐objective evolutionary algorithm which is based on an injection island approach. This approach is characterized by adopting an encoding of solutions which uses a different resolution for each island. This approach allows us to divide the decision variable space into well‐defined overlapped regions to achieve an efficient use of multiple processors. Also, this approach guarantees that the processors only generate solutions within their assigned region. In order to assess the performance of our proposed approach, we compare it to a parallel version of an algorithm that is representative of the state‐of‐the‐art in the area, using standard test functions and performance measures reported in the specialized literature. Our results indicate that our proposed approach is a viable alternative to solve multi‐objective optimization problems in parallel, particularly when dealing with large search spaces. Copyright © 2006 John Wiley & Sons, Ltd.     

Quasi sliding mode‐based single input fuzzy self‐tuning decoupled fuzzy PI control for robot manipulators with uncertainty

This paper presents a robust adaptive control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so‐called sliding mode control (SMC) approach. The motivation for using SMC in robotics mainly relies on its appreciable features. However, the drawbacks of the conventional SMC, such as chattering effect and required a priori knowledge of the bounds of uncertainties can be destructive. In this paper, these problems are suitably circumvented by adopting a reduced rule base single input fuzzy self tuning decoupled fuzzy proportional integral sliding mode control approach. In this new approach a decoupled fuzzy proportional integral control is used and a reduced rule base single input fuzzy self‐tuning controller as a supervisory fuzzy system is added to adaptively tune the output control gain of the decoupled fuzzy proportional integral control. Moreover, it is proved that the fuzzy control surface of the single‐input fuzzy rule base is very close to the input/output relation of a straight line. Therefore, a varying output gain decoupled fuzzy proportional integral sliding mode control approach using an approximate line equation is then proposed. The stability of the system is guaranteed in the sense of the Lyapunov theorem. Simulations using the dynamic model of a 3DOF planar manipulator with uncertainties show the effectiveness of the approach in high speed trajectory tracking problems. The simulation results that are compared with the results of conventional SMC indicate that the control performance of the robot system is satisfactory and the proposed approach can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances. Copyright © 2011 John Wiley & Sons, Ltd.     

基于BP算法的加权模糊Petri网权值学习算法

加权模糊Petri网缺乏较强的自学习能力,针对这个问题,给出了一个基于BP算法的加权模糊Petri网权值学习算法。该算法不需要对原有模型进行修改,使得加权模糊Petri网权值的学习和训练得到一定地简化。     

A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

 Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.     

Observer‐based Adaptive fuzzy control of time‐delay uncertain nonlinear systems

An observer‐based adaptive fuzzy model following controller is proposed for a class of MIMO nonlinear uncertain systems to cope with time‐delay, uncertainty in plant structure and disturbances. Based on universal approximation theorem the unknown nonlinear functions are approximated by fuzzy systems, where the premise and the consequent parts of the fuzzy rules are tuned with adaptive schemes. To have more robustness, and at the same time to alleviate chattering, an adaptive discontinuous structure is suggested. Moreover, the availability of the states measurement is not required and an adaptive observer is used to estimate the states. Asymptoic stability of the overall system is ensured using suitable a Lyapunov‐Krasovskii functional candidate. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Understanding mobile learning from the perspective of self‐regulated learning

Cognizant of the research gap in the theorization of mobile learning, this paper conceptually explores how the theories and methodology of self‐regulated learning (SRL), an active area in contemporary educational psychology, are inherently suited to address the issues originating from the defining characteristics of mobile learning: enabling student‐centred, personal, and ubiquitous learning. These characteristics provide some of the conditions for learners to learn anywhere and anytime, and thus, entail learners to be motivated and to be able to self‐regulate their own learning. We propose an analytic SRL model of mobile learning as a conceptual framework for understanding mobile learning, in which the notion of self‐regulation as agency is at the core. The rationale behind this model is built on our recognition of the challenges in the current conceptualization of the mechanisms and processes of mobile learning, and the inherent relationship between mobile learning and SRL. We draw on work in a 3‐year research project in developing and implementing a mobile learning environment in elementary science classes in Singapore to illustrate the application of SRL theories and methodology to understand and analyse mobile learning.     

Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index

Context adaptation (CA) based on evolutionary algorithms is certainly a promising approach to the development of fuzzy rule-based systems (FRBSs). In CA, a context-free model is instantiated to a context-adapted FRBS so as to increase accuracy. A typical requirement in CA is that the context-adapted system maintains the same interpretability as the context-free model, a challenging constraint given that accuracy and interpretability are often conflicting objectives. Furthermore, interpretability is difficult to quantify because of its very nature of being a qualitative concept. In this paper, we first introduce a novel index based on fuzzy ordering relations in order to provide a measure of interpretability. Then, we use the proposed index and the mean square error as goals of a multi-objective evolutionary algorithm aimed at generating a set of Pareto-optimum context-adapted Mamdani-type FRBSs with different trade-offs between accuracy and interpretability. CA is obtained through the use of specifically designed operators that adjust the universe of the input and output variables, and modify the core, the support and the shape of fuzzy sets characterizing the partitions of these universes. Finally, we show results obtained by using our approach on synthetic and real data sets.     

Guaranteed‐cost fuzzy controller design for a self‐sustaining bicycle with practical constraints

This study presents a guaranteed‐cost fuzzy controller for a self‐sustaining bicycle. First, the nonlinear dynamics of the bicycle are exactly transformed into a T‐S fuzzy system with model uncertainty. The guaranteed‐cost fuzzy controller is then designed for the transformed T‐S fuzzy system. For practical considerations, the input/state constraints are also satisfied in the design. The main contribution of this study is the guaranteed‐cost control design for a T‐S fuzzy system with model uncertainty and input/state constraints. Finally, simulation results show the validity of the proposed controller design method. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Observer‐based fault diagnosis and self‐restore control for systems with measurement delays

The problems of fault diagnosis and fault‐tolerant control are considered for systems with measurement delays. In contrast to the present fault diagnosis and fault‐tolerant control approaches, which consider only the input delay and/or state delay, the main contribution of this paper consists of proposing a new observer‐based reduced‐order fault diagnoser construction approach and a design approach to dynamic self‐restore fault‐tolerant control law for systems with measurement delays. First, the time‐delay system is transformed into a delay‐free system in form by a special functional‐based delay‐free transformation approach for measurement delays. Then, the fault diagnosis is realized online via the proposed reduced‐order fault diagnoser. Using the results of fault diagnosis, two dynamic self‐restore control laws are designed to make the system isolated from faults. A numerical example demonstrates the feasibility and validity of the proposed scheme. © 2012 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm

Three new learning algorithms for Takagi-Sugeno-Kang fuzzy system based on training error and genetic algorithm are proposed. The first two algorithms are consisted of two phases. In the first phase, the initial structure of neuro-fuzzy network is created by estimating the optimum points of training data in input-output space using KNN (for the first algorithm) and Mean-Shift methods (for the second algorithm) and keeps adding new neurons based on an error-based algorithm. Then in the second phase, redundant neurons are recognized and removed using a genetic algorithm. The third algorithm then builds the network in one phase using a modified version of error algorithm used in the first two methods. The KNN method is shown to be invariant to parameter K in KNN algorithm and in two simulated examples outperforms other neuro-fuzzy approaches in both performance and network compactness.     

基于改进的模糊神经学习算法的模糊规则优化

对“改进的模糊神经学习算法”(DevelopedNeuro-FuzzyLearningAlgorithm)简单介绍,并针对这一新算法的缺点,提出了新的聚类方法得到最佳的规则数,利用模糊权值优化规则来改进这个算法,降低算法的时间复杂度,简化神经网络。     

A survey of self‐healing systems frameworks

Rising complexity within multi‐tier computing architectures remains an open problem. As complexity increases, so do the costs associated with operating and maintaining systems within these environments. One approach for addressing these problems is to build self‐healing systems (i.e. frameworks) that can autonomously detect and recover from faulty states. Self‐healing systems often combine machine learning techniques with closed control loops to reduce the number of situations requiring human intervention. This is particularly useful in situations where human involvement is both costly to develop, and a source of potential faults. Therefore, a survey of self‐healing frameworks and methodologies in multi‐tier architectures is provided to the reader. Uniquely, this study combines an overview of the state of the art with a comparative analysis of the computing environment, degree of behavioural autonomy, and organisational requirements of these approaches. Highlighting these aspects provides for an understanding of the different situational benefits of these self‐healing systems. We conclude with a discussion of potential and current research directions within this field. Copyright © 2014 John Wiley & Sons, Ltd.