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The necessity of lowering the execution of system tests' cost is a consensual point in the software development community. The present study presents an optimization of the regression tests' activity, by adapting a test cases prioritization technique called Failure Pursuit Sampling-previously used and validated for the prioritization of tests in general-improving its efficiency for the exclusive execution...
This paper proposes a new approach for reactive power planning (RPP) or VAR Planning with two major steps. First, the fuzzy clustering algorithm is employed to select candidate locations for installing new shunt VAR sources. Second, a piecewise linear method is proposed for VAR capacity optimization via minimizing the total cost for system operation. In the cost minimization model, the tie-line total...
In the paper, the structure determination and parameter estimation for the non-linear systems are presented by means of the dynamic fuzzy model. The parameters estimation of fuzzy model is independent of each other by means of the orthogonal method. The most significant fuzzy rules are selected into the fuzzy model based on the “Innovation-Contribution” criterion and some other information criteria...
Temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. Several methods have been developed to explicitly construct temporal causal graphical models. However, how to best understand and conceptualize these complicated causal relationships is still an open problem. In this paper, we propose a decomposition approach to simplify the temporal...
Support vector ordinal regression (SVOR) is a recently proposed ordinal regression (OR) algorithm. Despite its theoretical and empirical success, the method has one major bottleneck, which is the high computational complexity. In this brief, we propose a both practical and theoretical guaranteed algorithm, block-quantized support vector ordinal regression (BQSVOR), where we approximate the kernel...
Ant colony optimization (ACO) is a kind of bionic swarm intelligence algorithm belongs to artificial intelligence (AI) field and has been successfully applied in resolving complex optimization problems. Support vector machine (SVM) is a new machine learning method with greater generalization performance, and has shown its superiority in classification and regression problems. By combining the advantages...
The current study presented a generalized regression neural network (GRNN) based approach to predict nitrogen oxides (NOx) emitted from coal-fired boiler. A novel 'multiple' smoothing parameters, which is different from the standard algorithm in which only single smoothing parameter was adopted (Matlab neural network toolbox, for example), were assigned to GRNN model. K-means clustering algorithm...
We present a new approach to parameters identification of the fuzzy regression model with respect to the e-insensitive estimator in this paper. The proposed method firstly employs the improved fuzzy c-mean clustering algorithm to carry out fuzzy partition of input-output data pairs, which ascertains the membership functions of fuzzy system. Secondly, the quadratic convex optimization similar to the...
Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent...
Fuzzy c-regression models (FCRM) performs switching regression based on a Fuzzy c-means (FCM)-like iterative optimization procedure, in which regression errors are also used for clustering criteria. In data mining applications, we often deal with databases consisting of mixed measurement levels. The alternating least squares method is a technique for mixed measurement situations, in which nominal...
This paper researches the possibility of using locally weighted algorithm for intelligent modeling of a nonlinear system for vanadium extraction in metallurgical process and proposes some optimized methods by finding the optimized regression coefficients by gradient descent and kernel function bandwidth by weighted distance. But kernel matrix computation for high dimensional data source demands heavy...
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