early warning systems are key components of strategies to reduce risk. this research, by adopting a systematic approach in the management of the risk of desertification and by including previously developed models and systems, offers an integrated efficient structure in terms of early warning for the risk of desertification as a pilot system for semi-arid areas of west golestan province in iran...

Using learning vector quantization (LVQ) network to construct a self-organizing fuzzy controller (SOFC) for multivariable nonlinear composite systems is developed in this paper. The LVQ network is used to provide information about the better locations of the IF-part membership functions through un-supervised learning. The generated fuzzy rule base is applied to the SOFC and updated by a self-le...

Fuzzy relation inequalities based on max-F composition are discussed, where F is a binary aggregation on [0,1]. For a fixed fuzzy relation inequalities system $ A circ^{F}textbf{x}leqtextbf{b}$, we characterize all matrices $ A^{'} $ For which the solution set of the system $ A^{' } circ^{F}textbf{x}leqtextbf{b}$ is the same as the original solution set. Similarly, for a fixed matrix $ A $, the...

Maintenance can be the factor of either increasing or decreasing system's availability, so it is valuable work to evaluate a maintenance policy from cost and availability point of view, simultaneously and according to decision maker's priorities. This study proposes a Partially Observable Markov Decision Process (POMDP) framework for a partially observable and stochastically deteriorating syste...

A case in case-based reasoning is a contextualized piece of experience, which can be represented in various forms. Traditional approaches can be classified into three main categories: feature vector representations, structured representations, and textual representations. More sophisticated approaches make use of hierarchical representations or generalized cases. For particular tasks such as de...

We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them, and combine them into a single feature vector which is classified by a support vector machine (SVM). The two global systems recognize faces by classifying a single feature vecto...

We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector...

Support vector machines (SVMs) proved to be highly efficient computational tools in various classification tasks. However, the knowledge learned by an SVM is encoded in a long list of parameter values, and it is not easy to comprehend what the SVM is actually computing. We show that certain types of SVMs are mathematically equivalent to a specific fuzzy–rule base, the fuzzy all–permutations rul...

This paper shows how automatic cases can be generated using statistical learning methods such as support vector machines. This work also introduces new conflict resolution and revision strategies for enhancing the performance of the Case Based Reasoning system. The input to this system is a set of training samples out of which automatic cases are generated using support vector machines. These c...

We investigate properties of kernel based regression (KBR) methods which are inspired by the convex risk minimization method of support vector machines. We first describe the relation between the used loss function of the KBR method and the tail of the response variable Y . We then establish a consistency result for KBR and give assumptions for the existence of the influence function. In partic...