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With ever-increasing power densities, Dynamic Thermal Management (DTM) techniques have become mainstream in today's systems. An important component of such techniques is the thermal trigger. It has been shown that predictive thermal triggers can outperform reactive ones. In this paper, we present a novel trade-off space of predictive thermal triggers, and compare different approaches proposed in the...
We propose a data mining approach to predict the wine's quality level in order to improve the quality of products for wine enterprises in this paper. A large dataset is considered and three regression techniques were applied. Through the comparison, we get the conclusion that the model established by neural network is more accurate and it can improve the quality of wine's production.
Non-stationary data distributions are a challenge in activity recognition from body worn motion sensors. Classifier models have to be adapted online to maintain a high recognition performance. Typical approaches for online learning are either unsupervised and potentially unstable, or require ground truth information which may be expensive to obtain. As an alternative we propose a teacher signal that...
Based on the data of household income of Shanghai low-rent housing families, a GM(1,1) forecast model and a Back-Propagation Artificial Neural Network (BPANN) forecast model are established respectively to predict the average household income of low-rent housing families. The comparison between the GM(1,1) and the BPANN model showed that the BPANN model is better than the GM(1,1) model at the aspects...
In order to improve tracking accuracy of the servo system, an adaptive inverse controller with PID feedforward is designed. It is based on the time-delay characteristic of the adaptive inverse control when training. The controller can realize accurately tracking of the servo, so as to meet the working need of the system. Finally, the tracking simulation is carried out on the digital servo experimental...
This paper investigates the Application of PID neural network(PIDNN) control law for a permanent magnet synchronous motor(PMSM) servo system. First, the math model of a PMSM is introduced for the PMSM servo system, on which PIDNN controller with on-line learning, tracking and regulating ability is applied. This PIDNN controller is compared with the conventional PID controller, and single neural PID...
Most of the mill concentrator determine the mill load according to the noise and the ball mill operating current, and it's low accuracy. So a mill load forecasting soft-sensor model based on the fundamental factors that reflecting the mill load is researched, Rough set theory is apply to optimization modeling data, applications RBF neural networks buliding mill load soft-sensor model and train the...
Process Neural Network (PNN) has an important significance in solving industry modeling problems which are related to time, but long time is cost on high dimension inputs nonlinear modeling problems. A new Improved Process Neural Networks based on KPCA and Walsh (IPNN-KPW) are proposed in this paper. KPCA method and discrete Walsh transform are used to reduce process neural network's time cost. Momentum...
Applying SRGMs (Software Reliability Growth Models) to real projects is a major concern in software reliability. Sometimes, it is hard to decide the best model for a specific project. Researchers have made a first step on solving this problem by combination, but the effect was limited in accuracy and adaptability. Aiming to improve the usability of the SRGMs, we propose a neural network based combination...
In the present work an attempt is made to develop a decision support system (DSS) using the pathological attributes to predict the fetal delivery to be done normal or by surgical procedure. The pathological tests like blood sugar (BR), blood pressure (BP), resistivity index (RI) and systolic/diastolic (S/P) ratio will be recorded at the time of delivery. All attributes lie within a specific range...
Nowadays practical solutions of engineering problems involve model-integrated computing. Due to their flexibility, robustness, and easy interpretability, the application of soft computing based models, may have an exceptional role. Despite of their advantages, the usage is still limited by their exponentially increasing computational complexity. Although, combining soft computing and anytime techniques...
The application of neural networks in the data mining has become wider and wider. Neural networks have high acceptance ability for noisy data, high accuracy and are preferable in data mining. We focus on the data mining process based on neural network. In this paper the data mining based on adaptive neural network is researched in detail, the key technology and ways to achieve the data mining based...
Aiming at the complex dynamic feature of large ship, an intelligent control structure based on library-similar knowledge-increasable neural network group is presented. This compounded control structure using the dynamic knowledge-increasable learning capability of the neural network groups, solve the problems of online identification and online design of the controller, so that the high precise output...
Aiming at the complex dynamic feature of large ship, an intelligent control structure based on library-similar knowledge-increasable neural network group is presented. This compounded control structure using the dynamic knowledge-increasable learning capability of the neural network groups, solve the problems of online identification and online design of the controller, so that the high precise output...
Aiming at the complex dynamic feature of large ship, an intelligent control structure based on library-similar knowledge-increasable neural network group is presented. This compounded control structure using the dynamic knowledge increasable learning capability of the neural network groups, solve the problems of online identification and online design of the controller, so that the high precise output...
Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture important regularities in the data. Several possible solutions were proposed, namely class based models, factored models, decision trees and neural networks. This paper describes improvements obtained in recognition of spoken...
There is an increasing interest in more accurate prediction of software maintainability in order to better manage and control software maintenance. Recently, TreeNet has been proposed as a novel advance in data mining that extends and improves the CART (classification and regression trees) model using stochastic gradient boosting. This paper empirically investigates whether the TreeNet model yields...
The kernel function and parameters selection is a key problem in the research of support vector machine. After discussing the influence of support vector machine on kernel parameters and error penalty factors, a new kernel function CombKer was proposed and constructed. The CombKer kernel function is a kind of combination kernel function, which combines the Gaussian RBF kernel function that has the...
In this paper, adaptive-tree-structure-based fuzzy model is applied to predict chaotic time series. The fuzzy partition of input data set is adaptive to the pattern of data distribution to optimize the number of the subsets automatically by binary-tree model. A fuzzy area around every discriminant edge is set up by the membership functions corresponding to every subset of input data. A complex nonlinear...
Recommendation-based trust models have emerged as an important risk management mechanism in E-commerce and online environments. This paper presents a trust model with recommendations management based on subjective logic and Bayesian estimation. For dealing with recommendations, the theory of subjective logic is used to express trust properties as beliefs and a method is provided for mapping between...
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