Optimizing of a Company’s Cost under Fuzzy Data and Optimal Orders Under Dynamic Conditions

The purpose of this article is to suggest tools of inventory management which would determine economically optimal order quantities. One of them is based on the so-called fixed order quantity model which takes into account several elements of inventory cost, such as ordering cost, transportation and storing cost, frozen capital cost, as well as extra discounts. The tool is based on fuzzy concepts represented by Ordered Fuzzy Numbers. The second tool takes into account the dynamics and works on the basis of replenishment system. This tool can be treated as a kind of controller. Examples of using this tools are presented.This work was supported by Bialystok University of Technology grant S/WI/2/2011Irena Sobol: [email protected] ; Dariusz Kacprzak: [email protected] Sobol, Department of Computer Science, Polish-Japanese Institute of Information Technology; Dariusz Kacprzak, Faculty of Computer Science, Bialystok University of Technology,5(71)17218

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

A CENTER MANIFOLD THEORY-BASED APPROACH TO THE STABILITY ANALYSIS OF STATE FEEDBACK TAKAGI-SUGENO-KANG FUZZY CONTROL SYSTEMS

The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system