A technique to perform design calculations on imprecise representations of parameters has been developed and is presented. The level of imprecision in the description of design elements is typically high in the preliminary phase of engineering design. This imprecision is represented using the fuzzy calculus. Calculations can be performed using this method, to produce (imprecise) performance parameters from imprecise (input) design parameters. The Fuzzy Weighted Average technique is used to perform these calculations. A new metric, called the γ-level measure, is introduced to determine the relative coupling between imprecise inputs and outputs. The background and theory supporting this approach are presented, along with one example. 1.

This paper is concerned with establishing broadly-based system-theoretic foundations and practical techniques for the problem of system identification that are rigorous, intuitively clear and conceptually powerful. A general formulation is first given in which two order relations are postulated on a class of models: a constant one of complexity; and a variable one of approximation induced by an observed behaviour. An admissible model is such that any less complex model is a worse approximation. The general problem of identification is that of finding the admissible subspace of models induced by a given behaviour. It is proved under very general assumptions that, if deterministic models are required then nearly all behaviours require models of nearly maximum complexity. A general theory of approximation between models and behaviour is then developed based on subjective probability concepts and semantic information theory The role of structural constraints such as causality, locality, finite memory, etc., are then discussed as rules of the game. These concepts and results are applied to the specific problem or stochastic automaton, or grammar, inference. Computational results are given to demonstrate that the theory is complete and fully operational. Finally the formulation of identification proposed in this paper is analysed in terms of Klir’s epistemological hierarchy and both are discussed in terms of the rich philosophical literature on the acquisition of knowledge. 1

Abstract—Ultrasonic measurements of human carotid and femoral artery walls are conventionally obtained by manually tracing interfaces between tissue layers. The drawbacks of this method are the interobserver variability and inefficiency. In this paper, we present a new automated method which reduces these problems. By applying a multiscale dynamic programming (DP) algorithm, approximate vessel wall positions are first estimated in a coarse-scale image, which then guide the detection of the boundaries in a fine-scale image. In both cases, DP is used for finding a global optimum for a cost function. The cost function is a weighted sum of terms, in fuzzy expression forms, representing image features and geometrical characteristics of the vessel interfaces. The weights are adjusted by a training procedure using human expert tracings. Operator interventions, if needed, also take effect under the framework of global optimality. This reduces the amount of human intervention and, hence, variability due to subjectiveness. By incorporating human knowledge and experience, the algorithm becomes more robust. A thorough evaluation of the method in the clinical environment shows that interobserver variability is evidently decreased and so is the overall analysis time. We conclude that the automated procedure can replace the manual procedure and leads to an improved performance. Index Terms—Artery, boundary detection, dynamic programming, imaging, ultrasonic. I.

Abstract — This paper introduces quantum neural networks (QNN’s), a class of feedforward neural networks (FFNN’s) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNN’s can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNN’s are capable of recognizing structures in data, a property that conventional FFNN’s with sigmoidal hidden units lack. Index Terms — Fuzzy classification, multilevel partitions, multilevel transfer functions, quantum neural networks, quantum

Fuzzy sets constitute the oldest and most reported soft computing paradigm. They are well-suited to modeling different forms of uncertainties and ambiguities, often encountered in real life. Integration of fuzzy sets with other soft computing tools has lead to the generation of more powerful, intelligent and efficient systems. In this position paper we seek to outline the contribution offuzzy sets to pattern recognition, image processing, and machine intelligence over the last 40 years.

We have developed a new computer algorithm for determining the backbone resonance assignments for biopolymers. The approach we have taken, relative hypothesis prioritization, is implemented as a Lua program interfaced to the recently developed computer-aided resonance assignment (CARA) program. Our program can work with virtually any spectrum type, and is especially good with NOESY data. The results of the program are displayed in an easy-to-read, color-coded, graphic representation, allowing users to assess the quality of the results in minutes. Here we report the application of the program to two RNA recognition motifs of Apobec-1 Complementation Factor. The assignment of these domains demonstrates AutoLinkÕs ability to deliver accurate resonance assignments from very minimal data and with minimal user intervention.

Complex High Autonomy Systems generally require the use of multiple modelling formalisms and multiple levels of abstraction in order to accurately and efficiently describe their dynamic characteristics. It is often necessary to integrate several modelling formalisms together if we want to reason about, simulate or analyze a system. Moreover, the use of a hierarchical representation helps us to more intelligently organize the models during development. We discuss a general modelling approach called Hybrid Heterogeneous Hierarchical Modelling (HHH Modelling) which supports multiple representations and hierarchical development of Knowledge-Based Autonomous System Simulations. In this context, we discuss methods to describe how different modelling formalisms may interact with each other in terms of data input/output and inter-model coordination (coordination between two different models). However, our major focus will be on intra-model coordination. Intra-model coordination is a method by...

Image features, such as edges, corners, and interesting points, are pixels identified by virtue of relations satisfied by the pixels within an image window. The classical edge operators, and the more elaborate operators for interesting points such as the Plessey and Moravec operators, have clear limitations. We introduce a new approach to the development of neural-fuzzy counterparts of such operators by using a training set comprising a set of pixels within a realistic image, these pixels being crisply scored by use of a classic operator. Our approach is directly compared with training NNs to fuzzy outputs, as proposed by Bezdek. Our method, which leads to relatively fast training, has the notable feature of being extensible over large windows.

The need for an automatic inference process able to deal with information coming from unreliable sources is becoming a relevant issue both on corporate networks and on the open Web. Mathematical theories to reason with uncertain information have been successfully applied in several situations, but each one of these models is tailored to deal with a specific semantics of uncertainty. In this paper, we put forward the idea of using explicit representations of the different types of uncertainty for partitioning the inference process into parts. By coordinating multiple independent reasoning processes, we are sometimes able to apply a specific model to each type of uncertain information, and recombine the final results via a suitable reconciliation process. We validated our approach applying it to the classic schema matching problem, and using the Ontology Alignment Evaluation Initiative, (OAEI) tests to assess the results.

In this paper state of the art of the fuzzy logic based visual object recognition systems is discussed. One of the major objectives of the computer vision is to recognize various objects from images. Application of fuzzy logic facilitates the smooth translation of ambiguous image information into natural language which can be processed by fuzzy set theory. Various methods of fuzzy object recognition are presented. Some rule based techniques are mentioned to show the applicability of object recognition in consumer electronics. A brief summary of fusion with neural networks and genetic algorithms is given.