Electrical and Computer Engineering

1. Introduction 2. Information granularity and Granular Computing 3. Formal approaches to information granulation: an overview and generalizations 3.1. Formal platforms of information granularity 3.2. Information granules of higher type and higher order 3.3. Hybrid models of information granules 4. A design of information granules 4.1. The principle of justifiable granularity and design of fuzzy sets 4.2. Information granules as constructs of fuzzy clustering 4.3. Design of information granules with knowledge hints 5.

In this paper, we introduce a general category of multi-fuzzy-neural networks (FNNs), analyze their underlying architecture and propose a comprehensive identification framework. The proposed multi-FNNs dwells on a concept of linear fuzzy inference-based FNNs. The design of the model uses a standard HCM (Hard C-Means) clustering algorithm and carries out an evolutionary fuzzy granulation of experimental data.

Abstract—Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches.

In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant. (Author)

Abstract We introduce a logic-driven clustering in which prototypes are formed and evaluated in a sequential manner. The way of revealing a structure in data is realized by maximizing a certain performance index (objective function) that takes into consideration an overall level of matching (to be maximized) and a similarity level between the prototypes (the component to be minimized).

Abstract In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, naive Bayes, and decision trees.

We introduce a concept of a fuzzy multiplexer, discuss its use in the design of logic networks and elaborate on the role of such networks in fuzzy modeling. In essence, a fuzzy multiplexer, fMUX, acts as a fuzzy switch whose output is determined on a basis of the logic values of the information inputs being switched (selected) by the select input. Multiplexers are generic building modules in digital systems. We show that networks of fuzzy multiplexers can play a similar role in fuzzy modeling.

In this paper, we propose a new category of current-mode łukasiewicz OR and AND logic neurons and ensuing logic networks along with their ultra-low power realization. The introduced circuits can operate in a wide range of the input signals varying in-between 10nA and 10μA. For low current values the operating point of transistors is set in the under threshold region.

An ancient Greek saying ''Change is the only constant''emerges as a universal wisdom that cuts across almost all facets of our life. Likewise, it becomes equally relevant in science and engineering when we commonly encounter phenomena of inherent dynamic behavior. This is also the case in Data Mining which over the last two decades has enjoyed a rapid development of its applications along with a plethora of advanced architectures and techniques.

Abstract Attribute reduction and reducts are important notions in rough set theory that can preserve discriminatory properties to the highest possible extent similar to the entire set of attributes. In this paper, the relationships among 13 types of alternative objective functions for attribute reduction are systematically analyzed in complete decision tables. For inconsistent and consistent decision tables, it is demonstrated that there are only six and two intrinsically different objective functions for attribute reduction, respectively.

Abstract In this paper, we present results of uncertain state estimation of systems that are monitored with limited accuracy. For these systems, the representation of state uncertainty as confidence intervals offers significant advantages over the more traditional approaches with probabilistic representation of noise.

While a genuine abundance of biomedical data available nowadays becomes a genuine blessing, it also posses a lot of challenges. The two fundamental and commonly occurring directions in data analysis deal with its supervised or unsupervised pursuits. Our conjecture is that in the area of biomedical data processing and understanding where we encounter a genuine diversity of patterns, problem descriptions and design objectives, this type of dichotomy is neither ideal nor the most productive.

Abstract Estimation of effort/cost required for development of software products is inherently associated with uncertainty. In this paper, we are concerned with a fuzzy set-based generalization of the COCOMO model (f-COCOMO). The inputs of the standard COCOMO model include an estimation of project size and an evaluation of other parameters. Rather than using a single number, the software size can be regarded as a fuzzy set (fuzzy number) yielding the cost estimate also in form of a fuzzy set.

Abstract New methodologies and better techniques are the rule in software engineering, and users of large and complex methodologies benefit greatly from specialized software support tools. However, developing such tools is both difficult and expensive, because developers must implement a lot of functionality in a short time. A promising solution is component-based software development, in particular package-oriented programming (POP). POP fails, however, to satisfy all the requirements of large, complex software engineering tasks.

Having a look at diverse interesting results obtained for fuzzy relational equations (for recent review see eg CU^ we can easily notice they have been derived under assumption the given fuzzy relational equation is solvable. Unfortunatelly, it is difficult to know a priori the equation has any solution. A situation becomes much more cumbersome while studying a system of equations, A1-R= B1

Abstract In many practical situations, we are faced with a necessity to combine sophisticated mathematical knowledge about the analyzed systems with informal expert knowledge. To make this combination natural, it is desirable to reformulate the abstract mathematical knowledge in understandable intuitive terms. In this paper, we show how this can be done for an abstract metric.

Resumen: We discuss the effects that the usual set theoretic and arithmetic operations with fuzzy sets and fuzzy numbers have with respect to the energies and entropies of the fuzzy sets connected and of the resulting fuzzy sets, and we also compare the entropies and energies of the results of several of those operations.

Abstract Meteorological volumetric radar data is used to detect thunderstorms that are responsible for most severe summer weather. Discriminating between different storm types is a difficult problem, however. A radar data processing system conducts a volume scan by stepping a continuously rotating antenna through a series of elevation angles at regular intervals. Systems exist that allow meteorologists to focus their attention on regions of interest within the radar scan known as storm cells.

Abstract While much research has been devoted to learning and machine intelligence, the field is still in its infancy. In particular, a technology that will allow for heuristic exploitation of information domain regularities to reduce the time required for knowledge acquisition while concomitantly resulting in an increase in the reliability of the acquired knowledge is still lacking. Unfortunately, contemporary learning mechanisms such as neural network architectures are inherently incapable of such performance.

Abstract The accurate estimation of software development effort has major implications for the management of software development in the industry. Underestimates lead to time pressures that may compromise full functional development and thorough testing of the software product. On the other hand, overestimates can result in over allocation of development resources and personnel [7].