This contribution presents an overview of the theoretical and practical aspects of the broad family of learning algorithms based on Stochastic Gradient Descent, including Perceptrons, Adalines, K-Means, LVQ, Multi-Layer Networks, and Graph Transformer Networks.

در این پژوهش سعی می‌­شود مدلی برای شبیه‌­سازی چرخه عمر صنعت برق با استفاده از شبیه‌سازی عامل­بنیان ارائه شود، مدل، 5 عامل استخراج شد و شبیه­‌سازی به کمک نرم‌افزار Anylogic صورت پذیرفت. بهینه‌­سازی مدل چهار سناریو نظر خبرگان شد. سناریوی نخست، جذابیت نیروگاه گازی سیکل ترکیبی کاهش یافت بر دو دیگر افزوده نتیجه افزایش تولید آبی است که توجه کمبود منابع کشور به‌صرفه نیست. دوم ورود یک فناوری جدید میزان...

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...

Health diagnosis of bearing is essential reduce the breakdowns of rotating machinery. An intelligent method to diagnose the bearing fault using vibration signal is proposed. This paper proposes a binary genetic algorithm (BGA) in feature selection process and discuss about the role of fitness functions in feature selection process by application of different fitness functions in GA process. A v...

A fully automatic procedure for brain tissue classification of single channel magnetic resonance images (MRI) of human Brain is described. Two different ANN classifiers namely Learning Vector Quantization (LVQ), Multilayer Perceptron (MLP) are used for segmentation (classification) of tissues in brain MR images. Each tissue type are segmented and assigned with different gray shades for represen...

Relevance Learning Vector Quantization (RLVQ) (introduced in [1]) is a variation of Learning Vector Quantization (LVQ) which allows a heuristic determination of relevance factors for the input dimensions. The method is based on Hebbian learning and defines weighting factors of the input dimensions which are automatically adapted to the specific problem. These relevance factors increase the over...

In this work the output density functions of hidden Markov models are phoneme-wise tied mixture Gaussians. For training these tied mixture density HMMs, modiied versions of the Viterbi training and LVQ based corrective tuning are described. The initialization of the mean vectors of the mixture Gaussians is performed by rst composing small Self-Organizing Maps representing each phoneme and then ...

To overcome the problem of invariant pattern recognition, Simard, LeCun, and Denker (1993) proposed a successful nearest-neighbor approach based on tangent distance, attaining state-of-the-art accuracy. Since this approach needs great computational and memory effort, Hastie, Simard, and Säckinger (1995) proposed an algorithm (HSS) based on singular value decomposition (SVD), for the generation ...

In the digital era we live in, efficient representation of data generated by a discrete source and its reliable transmission are unquestionable need. In this work we have focused on source coding taking image as source. Lattice Vector Quantization (LVQ) can be used for source coding as well as for channel coding. (LVQ) with Generator Matrix (GM) and codebook is implemented. When implementation ...

We propose a method to automatically determine the relevance of the input dimensions of a learning vector quantization (LVQ) architecture during training. The method is based on Hebbian learning and introduces weighting factors of the input dimensions which are automatically adapted to the speci c problem. The bene ts are twofold: On the one hand, the incorporation of relevance factors in the L...