Bilattice-based triangle provides an elegant algebraic structure for reasoning with vague and uncertain information. But the truth and knowledge ordering of intervals in bilattice-based triangle cannot handle repetitive belief revisions which is an essential characteristic of nonmonotonic reasoning. Moreover, the ordering induced over the intervals by the bilattice-based triangle is not sometimes intuitive. In this work, we construct an alternative algebraic structure, namely preorder-based triangle and we formulate proper logical connectives for this. It is also demonstrated that Preorder-based triangle serves to be a better alternative to the bilattice-based triangle for reasoning in application areas, that involve nonmonotonic fuzzy reasoning with uncertain information.

The sound detection and ranging (SODAR) system is an effective and active remote sensing tool to probe, in round-the-clock basis, the lower planetary boundary layer (LPBL) of the earth. The observations obtained from such SODAR systems can prove to be extremely useful for LPBL studies if classified and interpreted automatically. Manual interpretation of such data by an expert in this field is very tedious and error-prone. Hence, in this letter, we attempt to classify such data obtained from monostatic SODAR system, which is represented as echogram, by graph matching method. We initially convert the time domain's sample intensity variation, which is recorded as height versus time scale on echogram, to frequency domain for obtaining higher discriminating power of each sample of each class (as for example, thermal plume, inversion, etc.) of the echogram. For discriminating the individual class of the echogram, we use a measure of similarity. Thus, an expert system using graph matching method based on the frequency domain representation of each SODAR sample of each class is developed.

Our aim is to design a pattern classifier using fuzzy relational calculus (FRC) which was initially proposed by Pedrycz (1990). In the course of doing so, we first consider a particular interpretation of the multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. Subsequently, we introduce the notion of a fuzzy pattern vector to represent a population of training patterns in the pattern space and to denote the antecedent part of the said particular interpretation of the MFI. We introduce a new approach to the computation of the derivative of the fuzzy max-function and min-function using the concept of a generalized function. During the construction of the classifier based on FRC, we use fuzzy linguistic statements (or fuzzy membership function to represent the linguistic statement) to represent the values of features (e.g., feature F/sub 1/ is small and F/sub 2/ is big) for a population of patterns. Note that the construction of the classifier essentially depends on the estimate of a fuzzy relation /spl Rfr/ between the input (fuzzy set) and output (fuzzy set) of the classifier. Once the classifier is constructed, the nonfuzzy features of a pattern can be classified. At the time of classification of the nonfuzzy features of the testpatterns, we use the concept of fuzzy masking to fuzzify the nonfuzzy feature values of the testpatterns. The performance of the proposed scheme is tested on synthetic data. Finally, we use the proposed scheme for the vowel classification problem of an Indian language.

This correspondence elucidates the importance of similarity, as modeled by a measure of similarity, in approximate reasoning. A set of axioms is proposed to compute a reasonable measure of similarity between two imprecise concepts represented as fuzzy sets. For approximate reasoning, a similarity index between the fact and the antecedent of a rule is computed and is used in the reasoning mechanism. Accordingly, the existing reasoning mechanism is modified. A new similarity-based approximate reasoning methodology is proposed. As an illustration of its effectiveness, the proposed mechanism is used to develop a rule-based pattern classifier.

The authors consider the aspect of unconstrained algebraic restoration of a gray-level image, degraded due to defocusing, additive noise, etc., with additional nonnegative constraint on the individual pixel of gray-level image. The negative values in the gray level introduce absurdity in the entire representation. It is demonstrated that under certain conditions the constrained and unconstrained solutions for the restored image are the same. It is shown that using P. Wolfe's algorithm (1959) it is possible to handle efficiently the nonnegative constraint of the gray-level image. Necessary and sufficient conditions of the optimal constrained restoration were tested.>

A nonlinear multivariable steam generating unit (200 MW) has been decoupled using the existing decoupling theory. A set of fuzzy logic controllers, which virtually reduce to nonlinear multilevel relay elements, is designed for the decoupled subsystems to make the closed-loop subsystems asymptotically stable. Subsequently it has been shown, through digital simulation and field simulation, that the closed-loop subsystems track the desired state parameters under different operating conditions. A frequency domain stability analysis has been presented assuming that the decoupled subsystems are linear in nature and decoupling is accurate.

A hybrid (fuzzy and nonfuzzy) design concept has been proposed for the linear single-input single-output (SISO) system associated with fuzzy logic controllers. In the present design techniques, the concept ofL2-stability and its graphical interpretation, i.e. the circle criterion, are used. Thus the use of the classical Nyquist locus and the gain-phase chart are brought into the picture. The drawbacks of theL2-stability/circle criterion are indicated and overcome. In the present design technique, which is graphical in nature, the designer is given a feel for the system. Examples are provided to illustrate the application of hybrid controller's structure for the synthesis of the linear SISO system associated with the fuzzy logic controller.

The concept of the sector bound nonlinearity has been considered for frequency domain stability analysis of linear single-input single-output (SISO) and decoupled multi-input multi-output (MIMO) systems associated with the fuzzy logic controller. For analysis of the fuzzy logic controller, the system's model is considered to be available in transfer-function form, but for generation of the fuzzy logic controller no model description is necessary. A generalized Nyquist-type stability analysis is presented.