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This paper presents a novel nonlinear adaptive filter method, namely, Hammerstein adaptive filter with single feedback under minimum mean square error (HAF-SF-MMSE). A single delayed output is incorporated into the estimation of the current output based on minimum mean square error criterion, and therefore the history information of output is considered. Moreover, hybrid learning rates and adaptive...
Prolonged and sustained warming of the sea, acidification of surface water and rising of sea levels, creates significant habitat losses, resulting in the proliferation and spread of invasive species which immigrate to foreign regions seeking colder climate conditions. This is happening either because their natural habitat does not satisfy the temperature range in which they can survive, or because...
In order to solve the problem of the lack of prior knowledge in intrusion detection, as an unsupervised learning algorithm, the clustering algorithm is applied to intrusion detection. Aiming at the shortcomings of intrusion detection algorithm based on traditional hierarchical clustering, such as high time complexity and high false positive rate, a new clustering algorithm for intrusion detection...
Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e.g. Bayesian neural networks. Algorithms that implement adaptive ICA converge slower than their nonadaptive counterparts, however, they are capable of tracking changes in underlying distributions of input features. This intrinsically...
Implementing higher voltages in vehicles like 48V mild hybrid or full-hybrid enables CO2 reduction and weight savings. However, the increase in the voltage demands an accurate and robust protection system again potential fault conditions. Series arc is one of the fault conditions which needs to be detected and addressed before the benefits of using higher voltages in vehicle can be fully realized...
In daily life it is necessary to learn skills that can be applied in different tasks and different contexts. Usually these skills are acquired by observation or by direct physical training with another expert person. The critical point is to know which is the best possible way to achieve this knowledge acquisition. In this work we have proposed a collaborative environment where subjects with different...
The purpose of data mining is to explore, find and hence analyze relevant data from a massive data source using various technical means. This paper introduces the development of data mining to date, its functions, tasks and algorithms, as well as the process of data mining. The application and problems of data mining are also presented and finally the potential future development of data mining technology...
While there is a large amount of text data on the Internet, people need to organize the text data with experienced category. However, the flat structure of categories could not satisfy the modern information management. To solve this problem, we propose a hierarchical classification process with a strategy, called candidates, used to relieve the blocking problems. Besides, we establish the description...
Electric power SDH network is a comprehensive communication network, which takes advantages of SDH technology, and is a widely used technology. How to protect the network more effectively is the concern of the operators. So the main purpose of this paper is to find a way to analysis risk in advance and enhance reliability of electric power SDH network. Firstly, we set up an evaluation index system...
Scaling CMOS integrated circuit technology leads to decrease the chip price and increase processing performance in complex applications with re-configurability. Thus, VLSI architecture is a promising candidate in implementing neural network models nowadays. Backpropagation algorithm is used for training multilayer perceptron with high degree of parallel processing. Parallel computing implementation...
This paper develops a distributed stochastic subgrandient-based support vector machine algorithm when training data to train support vector machines are distributed in the network. In this situation, all the data are decentralized stored and unavailable to all agents and each agent has to make its own update based on its computation and communication with neighbors. With mild connectivity conditions,...
This paper investigates an event-triggered distributed cooperative learning (DCL) algorithm using radial basis function networks (RBFNs), where training samples are often extremely large-scale, high-dimensional and located on distributed nodes over strongly connected and weight-balanced networks. The algorithm is based on Zero-Gradient-Sum (ZGS) distributed optimization strategy and works in a fully...
Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled...
In this paper, we introduce a new dataset, Kimia Path24, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000x1000 (0.5mm x 0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset...
Web attacks are increasing and the scale of malicious URL continues to expand with the rapid development of the Internet, so that the network security situation is increasingly grim. In this case, this paper studies the URL multi-classification problem, which is a continuation of the reference [1] and follows the data sets and most of feature selection methods in it. Firstly, different types of URL...
With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone...
In order to satisfy the higher precision of indoor location-based service (ILBS), scholars have explored a great deal of algorithms based on Wi-Fi, ultrasonic, RFID or infrared, but all of which need additional device settings for transmitting and receiving signals before implementing location recognition. This paper proposed an idea that how to conveniently find the optimal feature or composite features...
Despite the popularity of Fingerprinting Localization Algorithms (FPS), general theoretical frameworks for their performance studies have rarely been discussed in the literature. In this work, after setting up an abstract model for the FPS, we show that a fingerprinting-based localization problem can be cast as a Hypothesis Testing (HT) problem and therefore various results from the HT literature...
This paper presents a new architecture for the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm targeting FPGA implementation. This new architecture offers higher efficiency and scalability in comparison to the existing methods. The proposed architecture is modeled and simulated using VHDL and is targeted at a Xilinx FPGA. Existing implementation architectures are also modeled and comparisons...
The rapid development of Internet technology has ushered in the era of information overload. How to pick out information with excellent quality and reduce unnecessary browsing time is a problem to be solved urgently. In order to recommend information that users might be interested in, this paper presents a new personalized recommendation algorithm with the quality of service (QoS) constraints based...
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