Vol. 24 · No. 6

Advancements in engineering modeling have changed the work of engineers during the last two decades. Sophisticated descriptions store information about shape oriented engineering objects and their relationships. Boundary representations of form features constitute shape models. Rules and checks have replaced simple data form of shape model entity attributes. This change of modeling facilitates a next step towards application of computer intelligence at engineering object related decisions. The authors propose a method of intelligent attribute definition for integrated decision assistance environments of modeling systems. This method provides quick and comprehensive assessment of situations for decisions on modification of modeled objects in very complex information environments. The paper starts with an outline of the approach to intelligent decision assistance by the authors. Next, an Internet portal communicated scenario of the proposed modeling is discussed. Following this, multilevel solution for modeling, adding characteristics for engineering objects, and definitions and communications are detailed as essential methods in the proposed modeling. Finally, behaviors for essential classes of modeled objects and an example for the definition of situations and behaviors represent implementation issues.

Markov chain models are efficient tools for representing stochastic discrete event processes with wide applications in decision and control. A novel approach to fuzzy-Petri-net reasoning generated solution to initial or another state in Markov-chain models is proposed. Reasoning is performed by a fuzzy-Petri-net supervisory controller employing a fuzzy-rule production system design and a fuzzy-Petri-net reasoning algorithm, which has been developed and implemented in C++. The reasoning algorithm implements calculation of the degrees of fulfilment for all the rules and their appropriate assignment to places of Petri net representation structure. The reasoning process involves firing active transitions and calculating degrees of fulfilment for the output places, which represent propositions in the knowledge base, and determining of fuzzy-distributions for output variables as well as their defuzzified values. Finally, these values are transferred to assign the state of Markov-chain decision model in terms of transition probabilities.

Missing data creates various problems in analysing and processing data in databases. In this paper we introduce a new method aimed at approximating missing data in a database using a combination of genetic algorithms and neural networks. The proposed method uses genetic algorithm to minimise an error function derived from an auto-associative neural network. Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks are employed to train the neural networks. Our focus also lies on the investigation of using the proposed method in accurately predicting missing data as the number of missing cases within a single record increases. It is observed that there is no significant reduction in accuracy of results as the number of missing cases in a single record increases. It is also found that results obtained using RBF are superior to MLP.

We define fuzzy implications in general, then study their families defined from t-norms, t-conorms and strong negations. Connections between such implications and negations are established. Some basic results are presented concerning the contrapositive symmetry property. The study gives birth to a new class of t-norms. Members of this family, together with the corresponding R-implications, have attractive properties making them competitive in different applications, especially in fuzzy inference rules.

Realizing that generality and uniformity of the usual Soft Computing (SC) structures exclude the application of plausible simplifications relevant in the case of whole problem classes resulted in the idea that a novel branch of soft computing could be developed by the use of which far simpler and more lucid uniform structures and procedures could be applied than in the traditional ones. Such a novel approach to computational cybernetics akin to SC was developed at Budapest Tech to control inaccurately and incompletely modeled dynamic systems under external disturbances. Hydraulic servo valve controlled differential cylinders as non-linear, strongly coupled multivariable electromechanical tools serve as excellent paradigms of such difficulties. Their control has to cope with the problem of instabilities due to the friction forces between the piston and the cylinder, as well as with uncertainties and variation of the hydrodynamic parameters that makes it unrealistic to develop an accurate static model for them. In this paper a combination of this novel method with the use of fractional derivatives is applied for the control of a hydraulic differential cylinder. Simulation results well exemplifying the conclusions are also presented.

Eco-grammar (EG-) systems are proposed as an example of a suitable formal framework for the study of some of the computationally relevant properties of the behavior of collections of embodied agents – called herds in this article – sharing a common environment, and acting in it in simple ways.

In system synthesis, one needs to derive from a given set of processes a system design which reflects exactly the functionalities of the processes and is free from erroneous situations such as deadlock and capacity overflow. This is especially important for shared-resource systems, in which errors are easily induced because of the sharing of common resources among different competing processes. In this paper, a synthesis method is proposed for designing shared-resource systems. It begins with specifying the given processes as augmented marked graphs. These augmented marked graphs are then synthesized through the fusion of common places which represents the shared resources. The net so obtained serves to represent the integrated system which reflects exactly the functionalities of the processes in the sense that the event sequences as well as the pre-conditions and post-conditions of each event occurrence are preserved. Based on the known properties of augmented marked graphs, the system properties such as liveness, boundedness and reversibility can be analysed effectively. The method is applied to manufacturing system design. Promising results are obtained.