Improving object-oriented methods by using fuzzy logic

The first and second order lattices in fuzzy logics are identified as generalization of the first and second order lattices in two-valued logic. In this context, the language of fuzzy theory are restated in order to show the flexibility gained by the relaxation of basic axioms of the two-valued logic. Example of knowledge representation are presented with Type I and Type II fuzzy sets and logics together with second order semantic uncertainty information contained within the boundaries of Type II fuzzy sets.

In this chapter, we aim to highlight how fuzzy logic can be a valid expressive tool to manage the software development process. We characterize a software development method in terms of two major components: artifact types and methodological rules. Classes, attributes, operations, and inheritance and part-of relations are examples of object-oriented artifact types. Each type is characterized by a set of properties whose values determine the membership of an artifact to other types. The relation between the property values and the membership values is defined by the heuristics that are typically expressed informally using textual forms in a natural language. The causal order among artifacts identifies the software process. Especially in the early phases of the development process, property values correspond to software engineer?s perceptions.

Chapter 6 - Classes of fuzzy set and logic Theories

This paper presents a new method of rule based fuzzy logic inferencing solely based on a many-valued logic, Lukasiewicz many-valued logic. Fuzzy logic can be considered as an extended set many-valued logic. Many-valued logic is extended from two-valued logic but differs from it in that each has different sets of primitives. The authors believe that fuzzy logic should be solely based on many-valued logic. In this paper, an implication connective of Lukasiewicz many-valued logic is proposed to be a basis of the conditional proposition in a new fuzzy logic inferencing method. Corresponding to this implication connective, a max-/spl delta/ composition is derived from Lukasiewicz many-valued logic and it is shown to fit human intuition. The advantage of the new fuzzy logic inferencing, over other fuzzy logic inferencing, is that it provides a solid theoretical basis for further study of its inferencing behavior when used in a certain application, such as in a fuzzy controller. An example, using machine vision to detect surface flaws of machined parts, is given. >

It is argued that two-valued logic and fuzzy logic both lack some representational and reasoning capability in modeling directly a social or natural world with a combination of positive, negative, and neutral relationships. This is due to the fact that the logic values in both lie in the interval (0, 1). An NPN (negative-positive-neutral) crisp logic and an NPN fuzzy logic are presented that assume, respectively, crisp or fuzzy values in the interval (-1, 1). The notions of NPN relations and NPN heuristic transitive closures are introduced. It is suggested that NPN logics can be embedded into NPN relations for cognitive process modeling. An application is given to illustrate the theory. >

Computer-aided design (CAD) tools aim to assist designers in their professional work, one key aspect of which is devising, evaluating, and choosing among multiple design alternatives. Yet, with few and limited exceptions, current tools handle just a single design model at a time, forcing users to adopt various ad hoc tactics for handling multiple design alternatives. Despite considerable prior work, there are no general, effective strategies for supporting design alternatives. New tools are needed to develop such strategies: to learn how designers’ behavior changes with support for multiple alternatives. In this article, we describe CAMBRIA, a multi-state prototype tool we developed for working with multiple 2D parametric CAD models in parallel. We describe the outcomes of an analytical evaluation of CAMBRIA using the Cognitive Dimensions framework.

An approach to proving theorems with fuzzy and not quite true argumentation is considered. Zadeh’s compositional rule of inference is used as the rule of provably correct reasoning, and its procedural implementation is enabled by a refutation mechanism. As such a mechanism, structural resolution (S-resolution) is proposed that is a generalization of the principle of resolutions to fuzzy statements. S-resolution is based on semantic indices of letters and their similarity. Semantic indices are essential in S-resolution. They contain data used as control information in the process of inference. And similarity implies finding letters to obtain an S-resolvent. Combining Zadeh’s compositional rule of inference and S-resolution allows, on the one hand, to withdraw the problem of correctness of resolvents in fuzzy logic and, on the other hand, to ensure the regularity of the process of a proof in two-valued and fuzzy logics.

This paper presents a method of constructing accurate nonlinear dynamic MIMO models using a class of fuzzy logic systems (FLSs). Generally, the design of FLS models involves identification of both the FLS structure (linguistic fuzzy IF-THEN rules) and parameter tuning of fuzzy membership functions. Most current techniques treat these parts separately, which may result in a suboptimal solution. The authors propose to optimize the two parts simultaneously using a genetic algorithm (GA), a stochastic global search method which explores the solution space in manner similar to natural evolution. In the authors' work new FLS domain specific operators are introduced that significantly reduce search time, and at the same time, increase the accuracy of results. The FLS model obtained from GA search is further fine-tuned using a conjugate gradient method. To illustrate the proposed method, the authors show that the FLS models compare well to other nonparametric modeling techniques, such as standard feedforward neural networks on an example of a nonlinear system. In addition, a FLS offers qualitative model description in terms of the linguistic fuzzy rules which may contribute to the understanding and control of the process. These merits make the FLS models attractive for use in model predictive control and other process applications.

Hypermedia systems development is, in many regards, different from "conventional" systems development, chief amongst these differences being its multidisciplinary nature. Foremost amongst the roles in hypermedia development are software engineering and graphic design. However, traditionally the tension between software engineers and graphic designers is pronounced. It is therefore important to gain an understanding of the differences between the two camps with a view to bringing them closer together. This paper reports on the findings of a survey of hypermedia developers conducted in Ireland. One of the objectives of the survey was to compare and contrast the development approaches, methods, and techniques used by software engineers with those used by graphic designers. It was found that software engineers and graphic designers are much closer than might be believed in their attitudes on the value and importance of processes and documented working methods. However, graphic designers primarily base development approaches around the use of specific tools, whereas software engineers are more reliant on traditional and object-oriented software development methods. Regarding diagramming methods, there is some evidence of cross-pollination, as software engineers often use informal techniques such as storyboarding and graphic designers use software engineering techniques such as use case diagrams, but graphic designers find software engineering techniques to be less useful than vice versa.

A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed‐loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

This study focuses on developing and implementing Mamdani hybrid fuzzy logic inference system (FIS) for transverse crack detection and fault diagnosis in a woven fiber laminated glass/epoxy composite beam using different vibration modes of natural frequencies. The shifting of vibration is attributed to the implication of cracks. These vibration signatures are fuzzified through hybrid fuzzy sets (triangular, trapezoidal, Gaussian) and scaled to crack location and depth using the fuzzy rules and defuzzification process. The vibration signatures are recorded using ABAQUS finite element (FE) simulation software for a fixed beam and are fed as input parameters to the developed FIS for computing the desired outputs. The realization for crack depth and position is experimentally verified through a Fast Fourier Transform (FFT) analyzer. The experimental results with simulated data show that fuzzy logic application detects crack positions and depth accurately at different levels. It is concluded that the hybrid FIS bears a close resemblance to the experimental analysis and also stands out as an effective method for crack detection in LCB over other standalone methods. The current method can be used as a cost-effective non-destructive technique for health monitoring and fault diagnosis of composite beam structures in any practical field.

Estimation models in software engineering are used to predict some important attributes of future entities such as software development effort, software reliability and programmers productivity. Among these models, those estimating software effort have motivated considerable research in recent years. Estimation by analogy is one of the attractive techniques in software effort estimation field. However, the procedure used in estimation by analogy is not yet able to handle correctly categorical data. In this paper, we propose a new approach based on reasoning by analogy, fuzzy logic and linguistic quantifiers to estimate effort when the software project is described either by categorical or numerical data. Fuzzy logic-based cost estimation models are more appropriate when vague and imprecise information is to be accounted for. Fuzzy systems try to behave just like the processes of the brain with a rule base. In our proposed method the effort and the cost can be estimated using the Fuzzy logic based analogy method and the cost can be calculated. The performance of the proposed system is calculated using MARE (Mean Absolute Relative Error).

Since the term “software engineering” was coined some 20+ years ago, [4] a number of definitions for both the practice and the practitioner, a “software engineer,” have been proposed. The definition from a recent report on undergraduate software engineering curricula [3] states:Software engineering entails the understanding and application of engineering principles, design skills, good management practice, computer science and mathematical formalism. …The Software Engineer must be able to estimate the cost and duration of the software development process, and to determine correctness and reliability and express them with a degree of precision meaningful to other engineers and informed clients. …In recent years, several advanced degree programs in “software engineering” have been instituted in the US and abroad. One example of this can be found at the SEI itself. Two of the goals of the SEI Education Program are to: (1) develop software engineering curriculum modules that identify, organize, and document the body of knowledge that could be taught in software engineering degree programs, and (2) design, develop, support, and maintain model curricula for undergraduate and graduate software engineering programs.The question remains, however, how much of this can be taught in a classroom setting or even in a design lab, and how much must be learned on the job or via some sort of apprentice program? What does Ada have to do with software engineering ? Can Ada exist without it? Can software engineering be done without Ada?There are those who believe the term “software engineering” is just plain wrong: [1]… I deeply resent the misuse of the word “engineering” in the compound term “software engineering.” Such misuse constitutes, in my opinion, a cheap, deceptive attempt to take advantage of the professional reputation and image developed by real engineers over a century or so … it constitutes theft of professional reputation.In 1986, I coordinated a similar panel for the ACM Computer Science Conference (CSC) [2]. The introduction from that session still holds true for this one, and is paraphrased and tuned below:Most universities prepare students for continued growth and development during a lifetime of employment, whether in an industrial or government setting. Since traditionally, such an education has implied better job opportunities, the university student believes that by studying computer science and/or software engineering, she2 will be assured of a good job at a reasonable salary performing interesting and challenging tasks.On the other hand, some universities prepare students for graduate education and research, placing a heavy emphasis on theory. From this perspective, the university student believes that upon completion of a computer science or software engineering degree, she will be able to choose where to pursue her research, at the university, industry, or government site of her choosing.Three years have passed; and I have found other group of articulate people to join us and present new and exciting viewpoints on the topic of software engineering, university education, and, of course Ada. Richard Fairley, who participated in that CSC panel, continues to represent the academic point of view; Marcia Finfer represents the industrial point of view; Garth Glynn also provides the industrial point of view with the interesting addition of evaluating graduate-level software engineering degree curricula; David Markus brings the perspective of the human resource issues; Lyn Plinta provides the view of the recent graduate dealing with veterans of the hacking era; and David Umphress represents the DoD interests.These panelists, bringing together a wealth of experience as educators, practitioners, and policy makers, provide insights from both the demand-side of software engineering and the supply-side, as they take a multi-faceted, multi-national look at industrial, academic, and government expectations of software engineering as it is taught and practiced today.

Selection of appropriate Software Development Life Cycle(s) can increase projects success. Depending on the selected SDLC (Software Development Life Cycle), one can decrease development time/cost, minimize overhead and risk exposure, manage uncertainty, improve quality, promote client relations, and provide better project tracking and control. Despite the benefits of using suitable SDLC(s), it is generally difficult to select the most appropriate one. There is not enough information in the literature about the criteria and how to take them into account to select appropriate SDLC(s). Frequently used SDLCs and their comparative properties were elicited from literature, Internet and experts according to the criteria to select appropriate SDLC(s). A Fuzzy Logic (FL) system was developed according to the elicited knowledge about the criteria and their affect on selecting suitable SDLCs in various cases. Fuzzy input/output variables, membership functions and fuzzy rules were defined and generated according to the elicited knowledge. The FL system was tested for various inputs and improved according to the test results.

Conventionally modelling and simulation of complex nonlinear systems has been to construct a mathematical model and examine the system’s evolution or its control. This kind of approach can fail for many of the very large non-linear and complex systems being currently studied. With the invention of new advanced high-speed computers and the application of artificial intelligence paradigms new techniques have become available. Particularly neural networks and fuzzy logic for nonlinear modelling and genetic algorithms [Goldberg, D. (1989)] and evolutionary algorithms for optimisation methods have created new opportunities to solve complex systems [Bai, Y., Zhuang H. and Wang, D. (2006)]. This paper considers issues in design of multi-layer and hierarchical fuzzy logic systems. It proposes a decomposition technique for complex systems into hierarchical and multi-layered fuzzy logic sub-systems. The learning of fuzzy rules and internal parameters in a supervised manner is performed using genetic algorithms. The decomposition of complex nonlinear systems into hierarchical and multi-layered fuzzy logic sub-systems reduces greatly the number of fuzzy rules to be defined and improves the learning speed for such systems. In this paper a method for combining subsystems to create a hierarchical and multilayer fuzzy logic system is also described. Application areas considered are - the prediction of interest rate, unemployment rate predication and electricity usage prediction. Genetic Algorithms can be used as a tool for design and generation of fuzzy rules for a fuzzy logic system. This automatic design and generation of fuzzy rules, via genetic algorithms, can be categorised into two learning techniques namely, supervised and unsupervised. In supervised learning there are two distinct phases to the operation. In the first phase each individual is assessed based on the input signal that is propagated through the system producing output respond. The actual respond produced is then compared with a desired response, generating error signals that are then used as the fitness for the individual in the population of genetic algorithms. Supervised learning has successfully applied to solve some difficult problems. In this paper design and development of a genetic algorithm based supervised learning for fuzzy models with application to several problems is considered. A hybrid integrated architecture incorporating fuzzy logic and genetic algorithm can generate fuzzy rules that can be used in a fuzzy logic system for modelling, control and prediction