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A new training scheme for neural-network-based controller for power electronics systems is proposed. It utilizes the circuit model of the power conversion stage (PCS) in the training process. The training algorithm is a distributed form of evolutionary computation, being able to run on a computer cluster equipped with multiple graphics processing units (GPUs). As a design example, a boost converter...
With the development of cloud computing technology, there are many scientists who want to perform their experiments in cloud environments. Because of the pay-per-use method, it is cost-optimal for scientists to only pay for the cloud services needed for their experiments. However, selection of suitable resources is difficult because they are composed of various characteristics. Therefore, a method...
The numerical value discretization is an important task of the data preprocessing phase within the intelligent data analysis. This process allows us to reduce the number of values (among other advantages) with which techniques work, reducing the computational cost when it comes to working with large amounts of data. In this paper a numerical value discretization technique is proposed. Specifically,...
A new change detection method for heterogeneous remote sensing images (i.e. SAR & optics) has been proposed via pixel transformation. It is difficult to directly compare the pixels from heterogeneous images for detecting changes. We propose to transfer the pixels in different images to a common feature space for convenience of comparison. For each pixel in the 1st image, it will be transferred...
Clustering techniques that group samples based on their attribute similarity have been widely used in many fields such as pattern recognition, feature extraction and malicious behavior characterization. Due to its importance, various clustering techniques have been developed with distributed frameworks such as K-means with Hadoop in Apache Mahout for scalable computation. While K-means requires the...
A novel and efficient Dendrite Ellipsoidal Neuron based on hyper-ellipsoids is proposed. By using the clustering algorithm k-means++, the method automatically sets an optimum number of dendrites and increases classification performance. The proposed network overcomes the actual Dendrite Morphological Neural Networks due to it changes hyper-boxes by hyper-ellipsoids that create smoother decision boundaries...
In recent years, the research on neural networks has been guided by the search of new mathematical frameworks, with the hope of finding new features, as geometric interpretation, for facing today problems or reducing the computational cost. In this paper we introduce a new Clifford Neuron [1], extending the conformai neuron, presented in [2] through the generalization of the geometric algebra of quadratic...
In 2006 Zhang and Zhou proposed a multilabel classification model based on the MLP network, which was subsequently improved by Grodzicki et al. This paper further improves both these approaches by introducing a scaling parameter responsible for maintaining a balance between the impacts of particular components of the MLP's error function in the training process. The newly-proposed parameter is autonomously...
We present a novel approach for large speech databases quantization. It uses an unsupervised iterative process to regulate a similarity measure to set the number of clusters and their boundaries, thus overcoming the shortcomings of conventional clustering algorithms such as k-Means and Fuzzy C-Means, which require a priori knowledge of the number of clusters and a similarity measure that follows the...
The self-organizing algorithm of Kohonen is well known for its ability to map an input space. That technique is named as Self-Organizing Map — SOM. A SOM can be trained in a short period of time with a few optimization techniques such as “winning” neurons search scope limit. In this paper we propose alternative options for improving the SOM learning speed. The basic idea of the proposed modification...
Internet users have to face to tremendous information from website. Clustering is a good solution to organize information. However, most clustering algorithms operate in the static situation. That means, it doesn't allow any incremental data. Certainly, this restrict is not fit to network environment, since data from internet is continuous increasing. Thus, an incremental clustering algorithm based...
Self-Organizing Maps (SOMs) are unsupervised neural networks that build data models. Neuron labeling attaches descriptive textual labels to the neurons making up a SOM, and is an important component of SOM-based exploratory data analysis (EDA) and data mining (DM). Several neuron labeling approaches tend to leave some neurons unlabeled. The interaction between unlabeled neurons and SOM model accuracy...
The imbalanced learning problem is becoming pervasive in today's data mining applications. This problem refers to the uneven distribution of instances among the classes which poses difficulty in the classification of rare instances. Several undersampling as well as oversampling methods were proposed to deal with such imbalance. Many undersampling techniques do not consider distribution of information...
Millions of people use email correspondence for communication across the globe and it is a critically vital application for many businesses. Considerable amount of unsolicited mail flows into user's mail boxes on a daily basis. A major negative aspect since the past decade has been bulk spam or phishing mail. Besides such unsolicited spam emails being wearisome for many email users, it also puts pressure...
Spike sorting is the problem of identifying and clustering neurons spiking activity from recorded extracellular electro-physiological data. This is important for experimental neuroscience. Existing approaches to solve this problem consist of three steps: spike detection, feature extraction, and clustering. In our method, we use Fisher discriminant based dictionary learning to learn dictionary, whose...
This paper describes two new algorithms for optimising the structure of trained Evolving Connectionist System (ECoS) artificial neural networks (ANN). It also presents the results of preliminary empirical evaluations of the algorithms. While ECoS are fast and efficient constructive ANN algorithms they can lose efficiency if they are allowed to grow too large. The algorithms presented in this paper...
The study of Restricted Boltzmann Machine(RBM) attracts considerable attentions in recent years. RBM training algorithm is an unsupervised learning method with many applications, moreover, it is the basic module in deep learning. Maximizing the log-likelihood by gradient ascent method, RBM training algorithm can approximate the probability distribution underlying the observing data. For a simple RBM...
Millions of computers are infected with bot malware, form botnets and enable botmaster to perform malicious and criminal activities. Intrusion Detection Systems are deployed to detect infections, but they raise many correlated alerts for each infection, requiring a large manual investigation effort. This paper presents a novel method with a goal of determining which alerts are correlated, by applying...
In our data driven world, categorization is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on annotated samples that are often difficult to obtain and training often overfits. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given the difficulty...
This paper describes a new hardware-efficient method and implementation for neural spike sorting based on selection of a channel-specific near-optimal subset of features given a larger predefined set. For each channel, realtime classification is achieved using a simple decision matrix that considers the features that provide the highest separability determined through off-line training. A 32-channel...
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