A mixed method for model order reduction of a linar, single input-single output systemis presented. The denominator of the original system is reduceedby using modified pole clustering techniques and fuzzy C-means clustering algorithm. The Chebyshev polynomial has been used for reducing the numerator of higher order transfer function.Then the result has been compared for boththe reduction techni...

We study the computational complexity of finding the next most preferred solution in some common formalisms for representing constraints and preferences. The problem is computationally intractable for CSPs, but is polynomial for tree-shaped CSPs and tree-shaped fuzzy CSPs. On the other hand, it is intractable for weighted CSPs, even under restrictions on the constraint graph. For CP-nets, the p...

A new design of non-fuzzy-networks based on hard c-means (HCM) are introduced in this paper. To generate the ruels and design the networks, we use HCM clustering algorithm. The premise part of the rules of the proposed networks is expressed by the hard partition of input space generated by HCM clustering algorithm. The partitioned local spaces indicate the rules of the proposed networks. The co...

We describe the application of a multilayer perceptron, polynomial neural networks, and a fuzzy decision model to the problem of check approval from incomplete data. A simple benchmark case was established as a performance metric to compare the various non-linear strategies that were implemented. An overall improvement of at least 10% was obtained in each of these methods.

In this thesis we consider reasoning with preferences in a number of different AI problems. We start with a specific area as, temporal reasoning, for which there is specialized reasoning machinery. In particular, we consider quantitative temporal constraints and we add preferences in such a context. We discuss the complexity for solving temporal constraint satisfaction problems with preferences...

In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering, however, the effectiveness of this extension vis-à-vis some generic methods of fuzzy clustering has neither been discussed in a complete manner nor the performance of cluster...

From the algorithm procedure presented in section 4 and the examples in this section, we can nd out it is easy to solve interval valued fuzzy relation equations by using fuzzy relation inequality system (8). The computation complexity of this algorithm is as same as that of the more commonly used algorithm 5] for fuzzy relation equations (1). So, the problem is more complicated than constant fu...

Mathematical models of physical systems often contain parameters with an imprecisely known and uncertain character. It is quite common to represent these parameters by means of random variables. Numerous methods have been developed to compute accurate approximations to solutions of equations with such parameters. This approach, however, may not be entirely justified when the uncertainty is due ...

In this paper, a homogeneous polynomial Lyapunov function (HPLF) is employed in robust observer-based Integrated Fault-Tolerant Control (IFTC) of nonlinear systems modelled by the fuzzy model (PFM). This makes design problem benefit from conservative reduction property functions (PLFs) and simultaneously avoid emergence non-convex terms due to differentiation matrix. As result, less sum square ...