Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, decision trees. In this paper, we offer new algorithm called fuzzy parameterized soft aggregation classifier (FPFS-AC) combine mod...

Our research focused on testing various characteristics of pairwise comparison (PC) matrices in controlled experiments. About 270 students have been involved in the test exercises and the final pool contained 450 matrices. Our team conducted experiments with matrices of different size obtained from different types of MADM problems. The matrix elements have been generated by different questionin...

The tables containing the optimal decisions obtained when solving real decision-making problems under uncertainty are often extremely large. This raises problems related not only to the storage and management of so much information, but also to the use of these tables for knowledge retrieval and reasoning explanation purposes. In this paper, we propose turning the tables into minimum storage mu...

Most decision-making and problem-solving tasks are too complex to be understood quantitatively. People perform such tasks using knowledge that is imprecise rather than precise. This study presents consistent fuzzy linguistic preference relations rather than fuzzy analytic hierarchy process (FAHP) to select computer integrated manufactory (CIM) systems. The proposed method yields decision matric...

In this paper a new technique named as Intuitionistic fuzzy max-min average composition method is proposed to construct the decision method for Medical Diagnosis using different types of Intuitionistic fuzzy soft matrices and its operations. Sanchez’s approach for decision making is studied and the concept is generalized by the application of Intuitionistic fuzzy soft set theory. Through a surv...

This paper is a sequel to Kurano et al [9], [10], in which the fuzzy perceptive models for optimal stopping or discounted Markov decision process is given. We proposed a method of computing the corresponding fuzzy perceptive values. Here, we deal with the average case for Markov decision processes with fuzzy perceptive transition matrices and characterize the optimal average expected reward, ca...

In symbolic model checking, decision diagrams are used to store all reachable states of a computer program. The size of decision diagrams is highly dependent on the used variable ordering of the underlying structure, a matrix that represents transitions and variables of the system. The model checker LTSmin [16] currently uses a custom implementation to find a good variable ordering, but with th...

This paper addresses the robust fault detection and estimation problem of nonlinear descriptor system with unknown inputs observers. The considered nonlinear descriptor system is transformed into an equivalent multi-models form by using the Takagi-Sugeno (T-S) approach. Two cases are considered: the first one deals with the multi-models with measurable decision variables and the second one assu...

We present an approach for solving constraint nets occurring in spatial inference using methods of Machine Learning. In contrast to qualitative spatial reasoning we use a metric description. Relations between pairs of objects are represented by parameterised homogeneous transformation matrices and numerical (nonlinear) constraints on the parameters. For drawing inferences we have to multiply th...