Abstract: Uncertainty inference is a key problem in artificial intelligence. The concept of including degree is introduced and some methods of generating including degrees are given. Some applications of including degrees to the retrieval and inference in expert systems are demonstrated.

Abstract: This paper presents the continued work of a previously proposed ANFIS (Adaptive Neuro-Fuzzy Fuzzy Inference System) architecture with emphasis on the applications to dynamic system identification. We demonstrate the use of ANFIS for the hair dryer modeling problem and compare its performance with the ARX model.

Abstract: This study proposes a fuzzy resource allocation model for project management in which a fuzzy relation on resource needs and a budget limit are described. The model can be solved in the form of crisp linear programming (LP) with /spl alpha/-cut. Despite the delivery routes of the teams, the results can express whether the project has sufficient or insufficient resources resulting from each activity.

Abstract: This paper presents a probabilistic version of generalized rough set models. It generalizes the standard algebraic and probabilistic rough set models in two aspects. An arbitrary binary relation is used instead of an equivalence relation. A probability function on the universe is used instead of computing probabilities from the cardinality of sets. Fundamental issues related to probabilistic rough set models are examined.

Abstract: By means of two general operations $\oplus$ and $\otimes$, called ``pan-operations'', we build a new kind of integral This formulation contains, as particular cases, both Choquet's and Sugeno's integrals

Abstract: The cutting stock problem it is of great interest in relation with several real world problems. Basically it means that there are some smaller pieces that have to be cut from a greater stock piece, in such a way, that the remaining part of the stock piece should be minimal. The classical solution methods of this problem generally need a great amount of calculation. In order to reduce the computational load they use heuristics. A newer solution method is presented in this paper, which is based on a genetic technique. This method uses a tree representation of the cutting pattern, and combines different patterns in order to achive patterns with higher performance. The combination of the cutting patterns is realized by a combined crossover mutation operator. An application of the proposed method is presented briefly in the end of the paper.

Abstract: Basic relations and operations of fuzzy multisets which are also called fuzzy bags are newly introduced and their theoretical properties are described using a grade sequence. Application of fuzzy multisets to fuzzy relational database systems is considered: handling of fuzzy multirelations and extension of a query language are discussed.

Abstract: A new application of fuzzy set theory to the problem of GPS positioning accuracy improvement is presented. We employed fuzzy processing on the C/A code stand-alone receiver and the DGPS receiver. The membership functions for the processing are determined by position dilution of precision (PDOP), signal-to-noise ratio (SNR) and the reliable factor of fixed position. We can select more accurate position fixes according to the values of the reliable factors. The accuracy of positioning has been improved by selecting position fixes from the original ones.

Abstract: In this paper, possibility programming problems are formulated to obtain fuzzy decisions that reflect vagueness in decision problems. According to the different decision cases, there are two options: one is linear programming (LP), the other is quadratic programming (QP). In general, it is feasible that QP will obtain a greater number of different solutions than LP will.

Abstract: It is often difficult to make accurate predictions, given uncertain and noisy data for classification. Unfortunately, most real-world problems have to deal with such imperfect data. This paper presents a new model for fuzzy classification by integrating fuzzy classifiers with decision trees. In this approach, a fuzzy classification tree is constructed from the training data set. Instead of defining a specific class for a given instance, the proposed fuzzy classification scheme computes its degree of possibility for each class. The performance of the system is evaluated by empirically compared with a standard decision tree classifier C4.5 on several benchmark data sets from the UCI machine learning repository.

Abstract: SIRMs (Single Input Rule Modules) Connected Fuzzy inference Model is proposed for multiple input fuzzy control. In the model, the importance degree is defined first and single input fuzzy rule module is constructed for each input item. The model output is obtained by summarizing the production of the importance degree and the fuzzy inference result of each module. The proposed model needs both very few rules and parameters and the rules can be designed much easier. Moreover, the role of each input item can be strengthened or weakened by changing its importance degree according to experts' intuitive experiences. The proposed model is applied to typical first order lag systems and second order lag systems to confirm the improvement in control performance compared with the conventional model.

Abstract: The default multilayer neural network topology is a fully interlayer connected one This simplistic choice facilitates the design but it limits the performance of the resulting neural networks The best-known methods for obtaining partially connected neural networks are the so called pruning methods which are used for optimizing both the size and the generalization capabilities of neural networks Two of the most promising pruning techniques have therefore been selected for a comparative study It is shown that these novel techniques are hampered by having numerous user-tunable parameters, which can easily nullify the benefits of these advanced methods Finally, based on the results, conclusions about the execution of experiments and suggestions for conducting future research on neural network pruning are drawn

Abstract: In previous papers, we presented an empirical methodology based on Neural Networks for obtaining fuzzy rules which allow a system to be described, using a set of examples with the corresponding inputs and outputs. Now that the previous results have been completed, we present another procedure for obtaining fuzzy rules, also based on Neural Networks with Backpropagation, with no need to establish beforehand the labels or values of the variables that govern the system.

Abstract: This paper describes a design environment for the hardware realizations of fuzzy controllers which includes a set of CAD tools to ease the description, verification and synthesis of this kind of systems. Special emphasis is focused on the use of a standard hardware description language (VHDL) and compatibility with other integrated circuits design tools.

Abstract: The aim of this paper is to show how J.A. Robinson's resolution principle was perceived and discussed in the AI community between the mid sixties and the first seventies. During this time the so called heuristic search paradigm was still influential in the AI community, and both resolution principle and certain resolution based, apparently human-like, search strategies were matched with those problem solving heuristic procedures which were representative of the AI heuristic search paradigm.

Abstract: In this paper we present an overview of mathematical models for handling partial entailments and their extensions in a probabilistic frame.

Abstract: In a Boolean Algebra B, an inequality f(x,x --> y)) = y satisfying the condition f(1,1)=1, is considered for defining operations a --> b among the elements of B. These operations are called Conditionals'' for f. In this paper, we obtain all the boolean Conditionals and Internal Conditionals, and some of their properties as, for example, monotonicity are briefly discussed.

Abstract: In the field of medical science, the extraction of the brain regions from MR images is valuable to diagnose an Alzheimer's disease. We propose here a novel approach to extract the brain region using the fuzzy matching technique. We describe a modeling of the intensity histogram by fuzzy logic and evaluate fuzzy matching techniques for the extraction of the brain region. We develop the extraction algorithm based on a standard region growing technique. An experimental result on 36 MRI data shows that the error rate is 2.4%, on the average, against manually extracted volumes by a medical doctor.

Abstract: A criteria trade-off analysis approach, based on relationships analysis for fuzzy decision-making, is proposed. The degrees of conflict and cooperation between any two individual criteria are first formulated. Relationships between individual criteria are identified based upon their conflicting and cooperative degrees. The criteria are converted into a disjunctive normal form to obtain a uniform representation of the criteria, and then arranged into a four-level hierarchical aggregation structure. A set of parameterized aggregation (fuzzy AND/OR) operators is selected to aggregate the judgements for the alternatives. A compromise alternative, which is proven to satisfy Pareto optimality, can thus be obtained based on the aggregation hierarchical structure.

Abstract: While the coupling of artificial of neural networks (ANN) and symbolic AI (SAI) is a strategy adopted in many hybrid systems, a real integration of the two methodologies has not been thoroughly investigated yet: so far, most hybrid systems have been viewed as just an engineering shortcut to solve complex problems in which one methodology alone seems too weak. In this paper, an approach to integrating ANN and SAI is presented. The basic idea explored here is that there is much more to hybrid models than a coupling of methodologies for the sake of avoiding engineering problems.

Abstract: Fuzzy clustering (c-means) is a widely known unsupervised clustering algorithm, but it can not guarantee to find the global minimum, because it approximates the minimum of an objective function by the iterative method in solving the differentiation problem, starting from a given point. For overcoming this drawback, we incorporate the genetic search strategies in the fuzzy clustering algorithm to explore the data space from a multiple-point concept. The direct application of the genetic algorithms to the fuzzy clustering is not suitable, because sometimes the data set is enormous. Under this situation, the chromosome would be too long, so a distributed approach to fuzzy clustering by genetic algorithms is proposed to divide the huge search space into many small ones. The simulation results show our algorithm works fine.

Abstract: Earlier a "new" rough set theory for very large databases was proposed by T.Y. Lin (1996). In this paper the authors attempt to evaluate the performance of such a rough set theory for a very large database. ORACLE, a relational database management system (RDBMS), is the market leader in open system databases. Windows NT has been growing into the data server environment and is a strong contender for decision support system applications with its open and cost-effective architecture. So ORACLE running under Windows NT was used in this report. The main goal of this research is to formulate a suitable rough set theory for very large databases.

Abstract: In this paper, we develop the fuzzy controller for an ABS (anti-lock braking system). The system models of the ABS and the fuzzy controller structure are discussed. Computer simulations are given to understand the effect of some important parameters.

Abstract: To improve the diagnosis accuracy of conventional dissolved gas analysis (DGA) approaches, this paper proposes an evolutionary programming (EP) based fuzzy system development technique to identify the incipient faults of the power transformers. In comparison to results of the conventional DGA and artificial neural network (ANN) classification methods, the proposed method has been verified to possess superior performance both in developing the diagnosis system and in identifying the practical transformer fault cases.

Abstract: The author discusses computer support of consensus reaching in a group of individuals under fuzzy preferences and a fuzzy majority, using a group DSS (GDSS), and supervised by a "super-individual", a moderator, who monitors and runs the process. For measuring how far the group is from "consensus", Kacprzyk and Fedrizzi's (1986, 1988, 1989, 1995) soft degree of consensus is employed viewed as a degree to which, say, most of the important individuals agree to almost all of the relevant options. Yager's (1988, 1996) ordered weighted averaging (OWA) operators for importance qualified data are used.

Abstract: There are many activities of both technology and its application in data mining and knowledge discovery in database area. In this paper, actual applications of data mining are presented from its development point of view, and a generalized conceptual model of data mining is proposed. Additionally, to make the data mining process more applicable, important notes at the performing process against real world problems are discussed.