2,979 publications from this institution
In this paper a brief introduction to hybrid intelligent systems is presented. Hybrid intelligent systems can be developed by a suitable combination of soft computing methodologies. In particular, the applications of hybrid intelligent systems to pattern recognition and intelligent manufacturing are briefly described. The importance of designing and implementing hybrid intelligent systems for real-world applications is highlighted.
In this paper, we present a technique to enhance transition delay and stuck-open fault testing in an LSSD environment. To reduce shift dependency in the scan path, thereby improving transition quality, a re-arrangement heuristic combined with a one level XOR network is proposed. The method is hierarchical, combining a simple re-arrangement, heuristic driven local reconfiguration, and finally a circuit modification to improve delay fault testing.
We introduce and investigate a class of neural architectures of polynomial neural networks (PNNs), discuss a comprehensive design methodology and carry out a series of numeric experiments. PNN is a flexible neural architecture whose topology is developed through learning; it is a self-organizing network. PNN has two kinds of networks, polynomial neuron-based and fuzzy polynomial neuron (FPN)-based networks, according to a polynomial structure. The essence of the design procedure of PN-based self-organizing polynomial neural networks(SOPNN) dwells on the group method of data handling. Each node of the SOPNN exhibits a high level of flexibility and realizes a polynomial type of mapping (linear, quadratic, and cubic) between input and output variables. FPN-based SOPNN dwells on the ideas of fuzzy rule-based computing and neural networks. Simulations involve a series of synthetic as well as experimental data used across various neuro-fuzzy systems. A detailed comparative analysis is also included.
Abstract The paper deals with a problem of decomposition of a binary fuzzy relation defined in the Cartesian product of a finite space. We propose an algorithm which produces the decomposition or indicates that the given relation is non-decomposable, within a finite sequence of steps. INDEX TERMS: Fuzzy relationdecomposition of relationfuzzy relation equation. Additional informationNotes on contributorsANTONIO DI NOLA Under the auspices of C.N.R. (G.N.S.A.G.A.), Italy. SALVATORE SESSA Under the auspices of C.N.R. (G.N.S.A.G.A.), Italy.
The existing clustering validity indexes (CVIs) show some difficulties to produce the correct cluster number when some cluster centers are close to each other, and the separation processing mechanism appears simple. The results are imperfect in case of noisy data sets. For this reason, in this study, we come up with a novel CVI for fuzzy clustering, referred to as the triple center relation (TCR) index. The originality of this index is twofold. On the one hand, a new fuzzy cardinality is built on the strength of the maximum membership degree, and a novel compactness formula is constructed by combining it with the within-class weighted squared error sum. On the other hand, starting from the minimum distance between different cluster centers, the mean distance as well as the sample variance of cluster centers in the statistical sense are further integrated. These three factors are combined by means of product to form a triple characterization of the relationship between cluster centers, and hence a 3-D expression pattern of separability is formed. Subsequently, the TCR index is put forward by combining the compactness formula with the separability expression pattern. By virtue of the degenerate structure of hard clustering, we show an important property of the TCR index. Finally, based on the fuzzy C -means (FCMs) clustering algorithm, experimental studies were conducted on 36 data sets (incorporating artificial and UCI data sets, images, the Olivetti face database). For comparative purposes, 10 CVIs were also considered. It has been found that the proposed TCR index performs best in finding the correct cluster number, and has excellent stability.
This study delivers a general overview of the theory and practice of fuzzy relational equations. We discuss various methods leading to the solutions of these equations starting from analytical approaches, moving through semi-analytic methods and finally elaborating on the neural-like style of finding solutions to relational constructs. The paper addresses important aspects of knowledge representation worked out by these equations and proposes a number of structural enhancements of the existing relational architectures.
Quite often, complex systems or phenomena are observed from various points of view yielding the particular subsets of data usually being composed of locally available attributes. Such datasets give rise to individual models. As is reflective of the local behavior of the system (global data), each model can produce different, albeit similar results. A critical issue is to aggregate the results coming from the individual models. In virtue of the diversity of the produced results, the aggregation process has to be reflective of this variety. Equally important is a way of quantifying the diversity of the individual results. In this article, we provide an efficient and original way of aggregation of the results by engaging a principle of justifiable granularity and in this manner leading to interval-valued results summarizing the results produced by a collection of models. We develop an overall design process and discuss the associated optimization mechanism leading to a granular fuzzy model of a global nature. The detailed scheme of the principle of justifiable granularity is discussed along with the related performance indexes; in particular, two modes of design of information granules are investigated. The quality of the granular model is quantified with the aid of the criteria of coverage and specificity.
We discuss a problem of synthesis and analysis of granular rules emerging in data mining. Two descriptors of the rules (that is relevance and consistency) being viewed individually and en block are introduced. The relevance of the rules is quantified in terms of the data being covered by the antecedents and conclusions standing there. While this index describes each rule individually, the consistency of the rule deals with the quality of the rule viewed vis-a-vis other rules. It expresses how much the rule "interacts" with others in the sense that its conclusion is distorted by the conclusion parts coming from other rules. We show how the rules are formed by means of fuzzy clustering and their quality can be evaluated in terms of the above indexes. Global characteristics of a set of rules are also discussed and related to the number of information granules being constructed in the data space.
In this paper we deal with fuzzy numbers that modelize uncertain quantities present in many fields of applications, such as man‐machine systems. Main attention is paid to inverse operations for fuzzy numbers which allow one to solve equations or systems of equations with fuzzy numbers. The relevance of the method proposed for the determination of parameters of fuzzy models is also stressed.
Accurate classification of biomedical data is often confounded by potentially imprecise class labels assigned by an external reference test. We present a gradation method using fuzzy set theory and a dispersion-adjusted similarity measure to assign, for each pattern in a design set, a degree of belongingness to each class. After training a classifier using this adjusted design set, its performance is measured using a validation set of patterns with their original class labels. We empirically demonstrate the effectiveness of this method using three publicly available biomedical datasets. Using the same classifier, we benchmark the results against the original datasets without gradation.
