We explore the issue of re ning an exis tent Bayesian network structure using new data which might mention only a subset of the variables Most previous works have only considered the re nement of the net work s conditional probability parameters and have not addressed the issue of re n ing the network s structure We develop a new approach for re ning the network s structure Our approach is base...

Limited presence of nodal and line meters in distribution grids hinders their optimal operation and participation in real-time markets. In particular lack of real-time information on the grid topology and infrequently calibrated line parameters (impedances) adversely affect the accuracy of any operational power flow control. This paper suggests a novel algorithm for learning the topology of dis...

We explore the issue of refining an exis­ tent Bayesian network structure using new data which might mention only a subset of the variables. Most previous works have only considered the refinement of the net­ work's conditional probability parameters, and have not addressed the issue of refin­ ing the network's structure. We develop a new approach for refining the network's structure. Our appro...

A learning genetic algorithm is proposed to solve the experimental parameters optimization problem. This method can not only enhance the efficiency of genetic algorithm through the pre-given user experience, but also improve the efficiency of genetic algorithm via learning the knowledge obtained from the optimization process. Experimental results suggest that the learning genetic algorithm can ...

This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. FuzzyUCS combines the generalization capabilities of UCS with the good interpretability of fuzzy rules to evolve highly accurate and understandable rule sets. Fuzzy-UCS is tested on a large collection of real-world problems, and compared to UCS and three highly-used machine l...

One popular learning algorithm for feedforward neural networks is the back.propagation (BP) algorithm which includes parameters: learning rate (1]), momentum factor (n) and steepness parameter (A.).The appropriate selections of these parameters have a large effect on the convergence of the algorithm. Many techniques that adaptively adjust these parameters have been developed to increase speed o...

One popular learning algorithm for feedforward neural networks is the backpropagation (BP) algorithm which includes parameters, learning rate (eta), momentum factor (alpha) and steepness parameter (lambda). The appropriate selections of these parameters have large effects on the convergence of the algorithm. Many techniques that adaptively adjust these parameters have been developed to increase...

s In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general descriptions of concepts from specific instances of these concepts. In many real life situations, however, new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of...