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Software-reliability models (SRMs) are used for the assessment and improvement of reliability in software systems. These models are normally based on stochastic processes, with the nonhomogeneous Poisson process being one of the most prominent model forms. An underlying assumption of these models is that software failures occur randomly in time. This assumption has never been quantitatively tested. Our contribution in this paper is to conduct an experimental investigation that contrasts random processes with nonlinear deterministic processes as a model for software failures. We study two sets of real-world software-reliability data using the techniques of chaotic time-series analysis. We have found that both appear to arise from a deterministic process, rather than a stochastic process, and that both show some evidence of chaotic dynamics. In addition, we have conducted a series of k-steps-ahead forecasting experiments in the datasets, pitting a number of well-known stochastic SRMs against radial basis function networks (RBFNs), which are deterministic in nature. The out-of-sample prediction results from the RBFNs showed an improvement of roughly 25% over the best of the stochastic models, for both of our datasets. Finally, we propose a causal model to explain these results, which hypothesizes that faults in a program are distributed over a fractal subset of the program's input space. Index Terms--Chaos theory, fractal sets, soft computing, software quality, software reliability, time-series analysis.

A new approach to measuring the similarity between 3D-curves is presented and two different approaches for representing 3D-curves are described. The approach allows the possibility of using strings, where each element is a vector rather than just a symbol.

A novel, perception-based method, called Automated Perceptions Network (APN), for automated construction of compact and interpretable models from highly noisy data sets is presented. The method is evaluated on yield data of two semiconductor products, and possible directions for the future use of automated perceptions in data mining and knowledge discovery.

A neuro-fuzzy approach for solving the monopulse amplitude direction-finding is proposed. Findings indicate that the proposed adaptive network based fuzzy inference system (ANFIS)-based solution minimizes output error over a considerable dynamical range of signal input power, and in all directions.

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The simulation results of a cart-pole balancing system and nonlinear system modeling have shown that 1) the compensatory neurofuzzy system can effectively learn commonly used fuzzy IF-THEN rules from either well-defined initial data or ill-defined data; 2) the convergence speed of the compensatory learning algorithm is faster than that of the conventional backpropagation algorithm; and 3) the efficiency of the compensatory learning algorithm can be improved by choosing an appropriate compensatory degree. Index Terms - Fuzzy logic, machine learning, neural networks, neurofuzzy systems.

In this paper, a generalization of parameter hypotheses tests used in statistical inference is presented. This leads to fuzzy hypotheses defined as imprecise predictions about the unknown parameter of a family of distribution functions in question, rather than exactly and sharply given assertions about its location in the parameter space. The notion of fuzzy partitions is discussed in the first part of this paper and details regarding their structure in relation to possibility theoretical interpretations are investigated subsequently. In the second part we introduce the notions of fuzzy tests and disclose the impacts of ill defined counter hypothesis (i.e., hypotheses, partly supporting the same subset of the parameter space) on the reliability of such a fuzzy test. An example demonstrates the consistency of the notion of fuzzy tests with the classical crisp case in statistical inference.

Dempster's upper and lower probabilities occur when dealing with multi-valued mappings and qualify as Shafer's belief and plausibility measures. In this paper we generalize the notion of upper and lower probabilities while maintaining their properties as belief and plausibility measures, and subsequently apply this concept on Kwakernaak's fuzzy random variables. It is shown how fuzzy random variables induce belief and plausibility functions and it is pointed out what relations exist between fuzzy probabilities and degrees of belief and plausibility. Finally we focus on empirical fuzzy random variables and investigate their asymptotic behavior in terms of belief and plausibility constraints and gain a framework for non-parametric statistical reasoning on the basis of uncertain evidences.

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown in this paper that the performance of fuzzy control systems may be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate an optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller.

A new approach for obtaining a typical value of a fuzzy set as a fuzzy integral is developed by replacing the classic Lebesgue measure with the "typicality" measure. A new method for representing and calculating the one-dimensional fuzzy integral with respect to arbitrary measure derived by monotonic increasing function is proposed and its applicability is illustrated with examples. Index Terms - Biased measure, fuzzy integral, Lebesgue measure, monotonic increasing functions, typical value.

A general framework for dealing with numerical measurements in an approximate, uncertain, or fuzzy environment is presented. A fuzzy measurement is defined. It possesses several unique properties, which arise from its physical nature and distinguish it from concepts such as the fuzzy number. These properties, which include the fuzzy correlation term and the fuzzy equality relation, follow directly from physical considerations. The introduction of the fuzzy correlation term provides a mathematical tool for representing any correlation relations, which may exist between different fuzzy measurements. The main function of the fuzzy correlation term is to eliminate, or filter out, measurement values that are unlikely, given other fuzzy measurements. Thus, using the fuzzy correlation term, the range of possible measurement values is limited by physical realities. The information represented by the fuzzy correlation term is shown to be of great value in providing a wider picture of reality than it is possible to obtain by simply considering individual fuzzy measurements. Arithmetic operations on fuzzy measurements and functions of fuzzy measurements are also discussed, leading to the derivation of the fuzzy Riemann integral and its applications. Index Terms--Fuzzy arithmetic, fuzzy correlation term, fuzzy equality, fuzzy integral, fuzzy measurements, fuzzy numbers.

Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDB includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach on two types of time series: stock-market data and weather data. Index Terms--Computational theory of perception, data mining, fuzzy association rules, knowledge discovery in databases, time series databases.

In this paper, we present a neural-networks-based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing, fuzzy computing, linguistic computing, and pattern recognition. The major issues include 1) how to make neural networks process both numerical and linguistic data in a data base, 2) how to convert fuzzy linguistic data into related numerical features, 3) how to use neural networks to do numerical-linguistic data fusion, 4) how to use neural networks to discover granular knowledge from numerical-linguistic data bases, and 5) how to use discovered granular knowledge to predict missing data. In order to answer the above concerns, a granular neural network (GNN) is designed to deal with numerical-linguistic data fusion and granular knowledge discovery in numerical-linguistic databases. From a data granulation point of view, the GNN can process granular data in a database. From a data fusion point of view, the GNN makes decisions based on different kinds of granular data. From a KDDM point of view, the GNN is able to learn internal granular relations between numerical-linguistic inputs and outputs, and predict new relations in a database. The GNN is also capable of greatly compressing low-level granular data to high-level granular knowledge with some compression error and a data compression rate. To do KDDM in huge data bases, parallel GNN and distributed GNN will be investigated in the future. Index Terms--Data compression, data fusion, data mining, distributed KDD, fuzzy logic, granular computing, knowledge discovery, linguistic computing, neural networks, parallel KDD, soft computing.

The expressions of "excluded middle" and "crisp contradiction" are reexamined starting with their original linguistic expressions which are first restated in propositional and then predicate forms. It is shown that, in order to generalize the truth tables and hence the normal forms, the membership assignments in predicate expressions must be separated from their truth qualification. In two-valued logic, there is no need to separate them from each other due to reductionist Aristotalean dichotomy. Whereas, in infinite (fuzzy) valued set and logic, the separation of membership assignments from their truth qualification forms the bases of a new reconstruction of the truth tables. The results obtained from these extended truth tables are reducible to their Boolean equivalents under the axioms of Boolean theory. Whereas, in fuzzy set and logic theory, we obtain a richer and more complex interpretations of the "fuzzy middle" and "fuzzy contradiction." Index Terms - Crisp contradiction, excluded middle, extended truth tables, fuzzy contradiction, fuzzy middle, fuzzy normal forms, linguistic expressions, propositional and predicate forms, reaffirmation, renegation.

Switched fuzzy systems can be used to describe the hybrid systems with fuzziness. However, the languages to describe the switching logic and the fuzzy subsystems are, in general, different, and this difference makes the system analysis and implementation hard. In this paper, we use differential Petri net (DPN) as a unified model to represent both the discrete logic and fuzzy dynamic processes. To exam the rationality of the representation, we prove the correctness of the representation for the discrete part and estimate the approximation accuracy of the representation for the dynamic part. Our work provides a way to analyze the switched fuzzy systems by checking the PN and to use the PN model for further system implementation. We demonstrate the benefits of our work via a case study. [ABSTRACT FROM AUTHOR]

Software quality is one of the main concerns of software users. Hence, software testing is an utterly important phase in the software development life cycle. Nevertheless, manual evaluation of program compliance with its specification may be prohibitively time consuming. As a remedy, several software testing systems are using an automatic oracle to confirm that the developed software complies with its specification and determine whether a given test case exposes faults. The use of artificial neural networks and info-fuzzy networks as automated oracles has been explored elsewhere. Nevertheless, there is not enough research comparing these two popular approaches to automated evaluation of the test outcome. This paper fills the gap and reports on a set of experiments designed to compare the two methods based on ROC curves, training time, and dispersion analysis. [ABSTRACT FROM PUBLISHER]

Web service composition is a distributed model to construct new web service on top of existing primitive or other composite web services. However, current service technologies, including proposed composition languages, do not address the reliability of web service composition. Thus, it is hard to predict the system reliability. In this paper, we propose a method to compute system reliability based on Service Component Architecture (SCA), a standard that provides a language-independent way to define and compose service components in the system. We first present a formal service component signature model with respect to the specification of the SCA assembly model, and then propose a language-independent dynamic behavior model for specifying the interface behavior of the service component by port activities. Then, the failure behaviors of ports are defined through the Nonhomogeneous Poisson Process (NHPP). Based on the semantics of ports, several rules have been generated to compute reliability of port expressions, thus the overall system reliability can be automatically computed. An Online Shop example from IBM web site is given to illustrate our method, together with a testing bed to calculate port reliability. [ABSTRACT FROM PUBLISHER]

The necessity of accurate localization in mobile robotics is obvious-if a robot does not know where it is, it cannot navigate accurately and reach goal locations. Robots learn about their environment via sensors. Small robots require small, efficient, and, if they are to be deployed in large numbers, inexpensive sensors. The sensors used by robots to perceive the world are inherently inaccurate, providing noisy, erroneous data, or even no data at all. Combined with estimation error due to imperfect modeling of the robot, there are many obstacles to successfully localizing in the world. Sensor fusion is used to overcome these difficulties-combining the available sensor data to derive a more accurate pose estimation for the robot. A feeling of 'ready-fire-aim'' pervades the discipline-filters are chosen on little to no information, and new filters are simply tested against a few peers and claimed as superior to all others. This is folly-the most appropriate filter is seldom the newest. This article provides an overview and in-depth tutorial of all modern robot localization methods and thoroughly discusses their strengths and weaknesses to assist a robot researcher in the task of choosing the most appropriate filter for their task. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]