این پژوهش با الهام گرفتن از نتایج یک طرح مطالعاتی کاربردی، رویکرد سلسله­‌مراتبی جهت پیگیری فرایند توسعه تأمین‌کنندگان و حمایت تصمیمات موجود در هر مراحل آن ارائه‌ می‌کند. ابتدا، زمینه‌های تأمین نیازمند سپس واجد شرایط هریک زمینه‌ها به کمک تصمیم‌گیری چندشاخصه بهترین-بدترین مشخص می‌گردند. معیارهای شناسایی نیز مرور مطالعات پیشین بهره‌گیری نظرات خبرگان حوزه‌ی خرید استخراج‌شده‌اند. درنهایت، مدل ریاضی ...

In this paper we present a new procedure for the design and optimization of a kind of artiicial neural networks (ANN) based on genetic algorithms (GAs). The method proposed here, called g-lvq , uses a genetic algorithm with variable-length genome and a vectorial tness to optimize Learning Vector Quantization (lvq) neural networks. The procedure optimizes simultaneously the classiication accurac...

Various types of neural networks may be used in multi dimensional classi cation tasks among them Bayesian and LVQ algorithms are interesting respectively for their performances and their simplicity of operations The large number of operations involved in such algorithms may however be incompatible with on line applications or with the necessity of portable small size systems This paper describe...

In the domain of classification tasks, artificial neural nets (ANNs) are prominent data mining methods. Paradigms like learning vector quantization (LVQ) and probabilistic neural net (PNN) are suitable classifiers. In this paper, new approaches of evolutionary optimized LVQs and PNNs are proposed. Their classification accuracy is compared with results of standard PNN and LVQ. The complex real-w...

The main goal of this paper is to compare the performance which can be achieved by two different hybrid approaches analyzing their applications’ potentiality on real world paradigms (speech recognition and medical diagnosis). We compare the performance obtained with (1) Multinetwork RBF/LVQ structure, we use involves Learning Vector Quantization (LVQ) as a competitive decision processor and Rad...

Recently, an extension of popular learning vector quantization (LVQ) to general dissimilarity data has been proposed, relational generalized LVQ (RGLVQ) [10, 9]. An intuitive prototype based classification scheme results which can divide data characterized by pairwise dissimilarities into priorly given categories. However, the technique relies on the full dissimilarity matrix and, thus, has squ...

Winner-Takes-All (WTA) prescriptions for Learning Vector Quantization (LVQ) are studied in the framework of a model situation: Two competing prototype vectors are updated according to a sequence of example data drawn from a mixture of Gaussians. The theory of on-line learning allows for an exact mathematical description of the training dynamics, even if an underlying cost function cannot be ide...

Prototype based classifiers so far can only work with hard labels on the training data. In order to allow for soft labels as input label and answer, we enhanced the original LVQ algorithm. The key idea is adapting the prototypes depending on the similarity of their fuzzy labels to the ones of training samples. In experiments, the performance of the fuzzy LVQ was compared against the original ap...