International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

The paper defines four fuzzy project scheduling models under inflation condition without or with budget limit. The activity time is described by fuzzy relation or fuzzy variables. The models can be solved in the forms of crisp LP or NLP after using the concept of α-cut.

Fuzzy control systems have proved their applicability in many areas. Their user-friend-liness and transparency certainly belong to their main advantages, and these two enable developing and tuning such controllers easily, without knowing their exact mathematical description. Nevertheless, it is of interest to know, what mathematical functions hide behind a set of fuzzy rules and an inference machine. For practical purposes it is necessary to consider real, implementable fuzzy control systems with reasonably low computational complexity. This paper discusses the problem of what types of functions are generated by realistic fuzzy control systems. In the paper the explicit formulae of the transference functions for practically important special cases are determined, controllers having rules with triangular and trapezoidal membership functions, and crisp consequents. Here we restrict our investigations to rules with a single input.

In this paper, we present a complete design framework for an adaptive multiple agent fuzzy constraint-based controller (MAFCC) based on fuzzy penumbra constraint processing in each fuzzy constraint subnetwork collaborating with a connected constraint network and its corresponding semantic modeling in a first-order predicate calculus (FOPC) language, with application to a complex hydraulic system. The concept of “multiple agent” and “fuzzy constraint subnetwork” in a complex control system is introduced and some basic definitions of penumbra fuzzy constraint processing in a constraint subnetwork and the collaboration with an overall connected constraint network and its semantic modeling are addressed. As a result, a human agent interacts with system agents and allows the constraints to be added or deleted on-line according to the constraints imposed from the outside environment. Near-optimal system performance is accomplished by restricting all the penumbra constraints to be satisfied in each constraint subnetwork simultaneously which are interconnected as a result of constraints that exist between each of them. Following the principle of constraint satisfaction and fuzzy local propagation reasoning, each individual system agent is now constrained to behave in a certain fashion as dictated by the overall constraint network. In addition, the constraint network in MAFCC system provides an update strategy which makes a real time adaptive hydraulic control for all 20 cities possible.

In this work we define a conjugate fuzzy Cauchy problem and discuss the major differences between an ordinary and a conjugate fuzzy Cauchy problems. A necessary condition for the existence of a fuzzy solution to the conjugate fuzzy Cauchy problem is proposed.

This paper proposes to use the method of principal components to reduce the dimensionality of input space and the B-splines to represent the membership functions of input variables. A model reduction strategy, which is based on Johansen’s optimality theorem, also suggested. The utility of this approach is illustrated using a fuzzy system modeling example.

In the domain of law, various real situations are expressed as relations and/or combinations of legal knowledge items (legal concepts, articles of law, etc). Such knowledge items (legal facts, events) cannot be precisely defined. Legal judgement is performed based on resemblance of legal knowledge and facts. In our system, vague legal knowledge is saved in the fuzzy relational database, and legal inference is realized as fuzzy inference. The target law for this system is the United Nations Convention on Contracts for the International Sale of Goods (CISG).

This paper proposes a four-layered fuzzy language acquisition network (FLAN) for acquiring fuzzy language. It can catch the intended information from a sentence (command) spoken in natural language with fuzzy terms. The intended information includes a meaningful semantic action and the fuzzy linguistic information of that action (for example, the phrase “move forward” represents the meaningful semantic action and the phrase “very high speed” represents the linguistic information in the fuzzy command “Move forward in a very high speed.”). The proposed FLAN has two important features. First, we can make no restrictions whatever on the fuzzy language input which is used to specify the desired information, and the network requires no acoustic, prosodic, grammar and syntactic structure. Second, the linguistic information of an action is learned automatically and it is represented by fuzzy numbers based on α-level sets. A supervised learning scheme is proposed to train the FLAN on fuzzy training data. This learning scheme consists of the mutual-information (MI) supervised learning algorithm for learning meaningful semantic actions, and the fuzzy backpropagation (FBP) learning algorithm for learning linguistic information. An experimental system is constructed to illustrate the performance and applicability of the proposed FLAN.

This section is maintained by Vladik Kreinovich. Please send your abstract or copies of papers that you want to see reviewed here to vladik@cs.utep.edu, or by regular mail to: Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA.