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Question-answering (QA) websites supply a quickly growing source of useful information in numerous areas. These platforms present novel opportunities for online users to supply solutions, they also pose numerous challenges with the ever-growing size of the QA community. QA sites supply platforms for users to cooperate in the form of asking questions or giving answers. Stack Overflow is a massive source of information for both industry and academic practitioners, and its analysis can supply useful insights. Topic modeling of Stack Overflow is very beneficial for pattern discovery and behavior analysis in programming knowledge. In this paper, we propose a framework based on the Latent Dirichlet Allocation (LDA) algorithm and fuzzy rules for question topic mining and recommending highlight latent topics in a community question-answering (CQA) forum of developer community. We consider a real dataset and use 170,091 programmer questions in the R language forum from the Stack Overflow website. Our result shows that LDA topic models via novel fuzzy rules can play an effective role for extracting meaningful concepts and semantic mining in question-answering forums in developer communities.

In this paper we develop a software reliability model for Artificial Intelligence (AI) programs. We show that conventional software reliability models must be modified to incorporate certain special characteristics of AI programs, such as (1) failures due to intrinsic faults, e.g., limitations due to heuristics and other basic AI techniques, (2) fuzzy correctness criterion, i.e., difficulty in accurately classifying the output of some AI programs as correct or incorrect, (3) planning-time versus execution-time tradeoffs, and (4) reliability growth due to an evolving knowledge base. We illustrate the approach by modifying the Musa-Okumoto software reliability growth model to incorporate failures due to intrinsic faults and to accept fuzzy failure data. The utility of the model is exemplified with a robot path-planning problem.

Introducing fuzzy logic in knowledge representation is a general technique to improve flexibility and performances of knowledge based and control software. Many researchers propose to introduce fuzzy logic representation in learning algorithms. Interesting features arise when fuzzy sets substitute the interval-based classification of input in a learning system; some of them imply an improvement in performance others an increased structural complexity in the architecture of the system and in the learning process. Focusing on Learning Classifier Systems, the introduction of fuzzy logic produces some new interesting features in this class of learning algorithms from many points of view: a new approach to classifier competition, the birth of competition vs. cooperation dilemma, and the introduction of an appropriate fuzzy interface with external world. In this paper, we discuss a fuzzyfication of the classical architecture of a learning classifier system (Holland's approach) and the improvements deriving from the use of fuzzy logic. In this work we especially discuss the competition vs. cooperation dilemma, analyzing the influence of exploration policy on the performance of crisp and fuzzy versions of learning classifier systems. We mainly focus on the use of fuzzy classifier systems to implement behaviors for reactive autonomous agents in the mobile robotics domain.

In this paper a direct adaptive control algorithm based on a neural network NN as controller and a fuzzy inference system FIS as control error estimator is presented for a class of SISO uncertain nonlinear systems. The weights adaptation laws are based on the control error. A fuzzy inference system is used to provide an estimate of this error based on past history of the system behavior. The stability of the closed loop is studied using Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed approach.

Automated offline handwritten character recognition involves the development of computational methods that can generate descriptions of the handwritten objects from scanned digital images. This is a challenging computational task, due to the vast impreciseness associated with the handwritten patterns of different individuals. Therefore, to be successful, any solution should employ techniques that can effectively handle this imprecise knowledge. Fuzzy Logic, with its ability to deal with the impreciseness arisen due to lack of knowledge, could be successfully used to develop automated systems for handwritten character recognition. This paper presents an approach towards the development of a customizable fuzzy system for offline handwritten character recognition.

Anticipatory systems are systems whose change of state is based on information about present as well as future states. Planning and acting on the basis of anticipations of the future is an omnipresent feature of human control strategies, deeply permeating our daily experience and considered as the hallmark of natural intelligence. Yet, as the eminent mathematical biologist Robert Rosen has pointed out in his book Anticipatory Systems (1985), such control strategies are curiously absent from existing formal approaches to automatic control and decision-making processes. Recent developments in biology, ethology and cognitive sciences, however, as well as advancements in the technology of computer-based predictive models, compel us to reconsider the role of anticipation in intelligent systems and to the extent possible incorporate it in our formal approaches to control. Significant improvements in neural predictive computing when combined with the flexibility of fuzzy systems, supports the development of neurofuzzy anticipatory control architectures that integrate planning and control sequencing functions with feedback control algorithms. A review of the role of anticipation in intelligent systems and a new approach for neurofuzzy anticipatory control using radial basis neural predictive models and fuzzy if/then rules is presented.

We give the expression for the solution to some particular initial value problems in the space E¹ of fuzzy subsets of ℝ. We deduce some interesting properties of the diameter and the midpoint of the solution and compare the solutions with the corresponding ones in the crisp case.

In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.

Fuzzy modeling is one of the most known and used techniques in different areas to model the behavior of systems and processes. In most cases, as in data-driven fuzzy modeling, these fuzzy models reach a high performance from the point of view of accuracy, but from other points of view, such as complexity or interpretability, they can present a poor performance.

Several approaches are found in the bibliography to reduce the complexity and improve the interpretability of the fuzzy models. In this paper, a post-processing approach is carried out via rule selection, whose aim is to choose the most relevant rules for working together on the well-known accuracy-interpretability trade-off. The rule relevancy is based on Orthogonal Transformations, such as the SVD-QR rank revealing approach, the P-QR and OLS transformations. Rule selection is carried out using a genetic algorithm that takes into account the information obtained by the Orthogonal Transformations. The main objective is to check the true significance, drawbacks and advantages of the rule selection based on the orthogonal transformations via the rule firing strength matrix.

In order to carry out this aim, a neuro-fuzzy system, FasArt (Fuzzy Adaptive System ART based), and several case studies, data sets from the KEEL Project Repository, are used to tune and check this selection of rules based on orthogonal transformations, genetic selection and accuracy-interpretability trade-off. This neuro-fuzzy system generates Mamdani fuzzy rule based systems (FRBSs), in an approximative way. NSGA-II is the MOEA tool used to tune the proposed rule selection.

In this paper the equivalence between fuzzy systems and two nonparametric techniques for pattern recognition is considered. The conditions under which a fuzzy system coincides with the nearest neighbor rule, and with the Parzen’s classifier have been formulated.

Image processing and analysis in fuzzy set theoretic framework is addressed. Various uncertainties involved in these problems and the relevance of fuzzy set theory in handling them are explained. Different image ambiguity measures based on fuzzy entropy and fuzzy geometry of image subsets are mentioned. A discussion is made on the flexibility in choosing membership functions. Illustrations of commonly used fuzzy image processing operations such as enhancement, edge detection segmentation, skeleton extraction, feature extraction are then provided, along with their significance and characteristics. Their applications to some real life problems, e.g., motion frame analysis, remotely sensed image analysis, modeling face images are finally described. An extensive bibliography is also provided.

In this paper, a robust adaptive fuzzy controller is presented for a wide class of perturbed uncertain nonlinear system with unknown virtual control gain function (UVCGF). The Mamdani fuzzy system is used to approximate unstructured uncertain functions in the system. The proposed algorithm, which incorporated Nussbaum-type gain into the decoupled backstepping approach, does not require a priori knowledge of the sign of UVCGF, and circumvents the controller-singularity problem gracefully in some existing literatures. It proved that the tracking error can be driven to a small residual set while keeping all signals in the closed-loop system semi-globally uniformly ultimately bounded (SGUUB). Simulation results are presented to validate the effectiveness of the proposed controller.

This paper is concerned with the stability problem of nonlinear interconnected systems. Each of them consists of a few interconnected subsystems which are approximated by Takagi–Sugeno (T–S) type fuzzy models. In terms of Lyapunov's direct method, a stability criterion is derived to guarantee the asymptotic stability of interconnected systems. It is shown that the stability analysis problems of nonlinear interconnected systems can be reduced to linear matrix inequality (LMI) problems via suitable Lyapunov functions and T–S fuzzy techniques. Finally, numerical examples with simulations are given to demonstrate the validity of the proposed approach.

Artificial immune systems are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. Since the immune response must correctly allocate the available resources in order to attack an antigen with its best available antibody while trying to learning an even better one, the reproduction rate of each immune cell must be carefully determined. This paper presents a novel fuzzy inference technique to calculate the suitable number of clones for immune inspired algorithms that uses the clonal selection process as the evolutionary process. More specifically, this technique is applied to the CLONALG algorithm for solving pattern recognition tasks and to the copt-aiNet algorithm for solving combinatorial optimization tasks, particularly the Traveling Salesman Problem. The obtained results show that the fuzzy approach makes it possible to automatically determine the number of clones in CLONALG and copt-aiNet, thus eliminating this key user-defined parameter.

It is essential to control accurately the critical variables in patients whose normal control systems have been faced with trouble. Vital parameters were different for different people; also with respect to similar changes in the parameters, the responses were different. This issue led to changes in the model parameters. It should be noted that even a small change in some of the model parameters can lead to death of patients. Therefore, the purpose of this study was to design a controller which was robust with respect to model parameter changes and disturbances entered to the system; this controller must be able to control their blood sugar levels in a suitable settling time. One of the issues discussed in this paper is type-2 fuzzy method, which is to regulate blood glucose levels. The use of type-2 fuzzy systems allows us to model the effects of uncertainty in the systems which are based on special rules. It also gives us an opportunity to minimize the uncertainty effects, but unfortunately, because of the complexity in use and comprehension of type-2 fuzzy sets than type-1, they were less used. These complexities are due to its three-dimensional nature and the direct dependence of its relations to the development principles; this is how its computational complexity is made.

This paper discusses the question how the membership functions in a fuzzy rule based system can be extracted without human interference. There are several training algorithms, which have been developed initially for neural networks and can be adapted to fuzzy systems. Other algorithms for the extraction of fuzzy rules are inspired by biological evolution. In this paper one of the most successful neural networks training algorithm, the Levenberg-Marquardt algorithm, is discussed, and a very novel evolutionary method, the so-called “bacterial algorithm”, are introduced. The class of membership functions investigated is restricted to the trapezoidal one as it is general enough for practical applications and is anyway the most widely used one. The method can be easily extended to arbitrary piecewise linear functions as well. Apart from the neural networks and evolutional algorithms, fuzzy clustering has also been used for rule extraction. One of the clustering-based rule extraction algorithms that works on the projection of data is also reported in the paper.

Most methods of fuzzy rule based system identification either ignore feature analysis or do it in a separate phase. In this chapter we propose a novel neuro-fuzzy system that can simultaneously do feature analysis and system identification in an integrated manner. It is a five-layered feed-forward network for realizing a fuzzy rule based system. The second layer of the net is the most important one, which along with fuzzification of the input also learns a modulator function for each input feature. This enables online selection of important features by the network. The system is so designed that learning maintains the non-negative characteristic of certainty factors of rules. The proposed method is tested on both synthetic and real data sets and the performance is found to be quite satisfactory.

This chapter introduces a new family of neuro-fuzzy systems suitable for pattern recognition. These architectures are based on the Adaptive Resonance Theory (ART) but introducing formalisms from the Fuzzy Sets theory, in order to solve some theoretical leaks present in Fuzzy ART based models. As a main result, a duality between neural network and fuzzy system can be seen in the proposed FasArt family architectures.

The FasArt model is the kernel model of this family. The rest of the models have been developed in order to cope with particular features of typical pattern recognition problems: RFasArt is a recurrent version of FasArt that works with pattern structured sequences and it has been applied to the document recognition problem; FasArt with the combination of the STORE memory module has been applied to the problem of on-line handwriting recognition, which concerns sequences of subpatterns. Distributed FasArt deals with the problem of category proliferation present in all ART systems, such as FasArt family. The proposed architectures are applied to several important pattern recognition problems, which are also described.

As a first step toward standardization of a practical programming language for fuzzy system applications, we proposed Fuzzy system Description Language (FDL) in 1996. The specification of the first version of FDL was not definitive edition. This specification was designed for hardware-coding of fuzzy control systems based on fuzzy inference as a prerequisite. So although it fulfills the intended functions, several problems arise for unexpected applications.

In this article, we first describe the specification of standardized FDL with its background and properties. Then, we consider some problems (the assignment operation, the comparison operation and the internal expressions, etc.) arised from wide applications of FDL. We describe the improvements of FDL. At last, we describe the fuzzy inference systems based on the indirect inference method with FDL and discuss some properties.

Recently, Yong-Ming Li proposed fuzzy systems based upon genuine many-valued implications for SISO cases. From the opinion of application, it is expected that the fuzzy systems have monotonicity if both the antecedent and consequent parts of fuzzy rules are monotone. Hence we discuss the monotonicity of this fuzzy systems in this paper. However, some instances show that the fuzzy systems do not necessarily have monotonicity even if both antecedent and consequent parts of fuzzy rules have monotonicity. And then we give the sufficient conditions that the fuzzy systems based upon R-implications, some S-implications and some QL-implications have monotonicity. It is pointed out that our proofs are different with, strictly speaking, more complex than H. Seki's proofs because there does not exist the uniform formula of output in Li's system while H. Seki's proofs have them.