Dimensions of Neural-symbolic Integration - A Structured Survey

Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. We present a comprehensive survey of the field of neural-symbolic integration, including a new classification of system according to their architectures and abilities.Comment: 28 page

KNOWLEDGE-BASED NEURAL NETWORK FOR LINE FLOW CONTINGENCY SELECTION AND RANKING

The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution

Naive Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages

Web classification has been attempted through many different technologies. In this study we concentrate on the comparison of Neural Networks (NN), Naïve Bayes (NB) and Decision Tree (DT) classifiers for the automatic analysis and classification of attribute data from training course web pages. We introduce an enhanced NB classifier and run the same data sample through the DT and NN classifiers to determine the success rate of our classifier in the training courses domain. This research shows that our enhanced NB classifier not only outperforms the traditional NB classifier, but also performs similarly as good, if not better, than some more popular, rival techniques. This paper also shows that, overall, our NB classifier is the best choice for the training courses domain, achieving an impressive F-Measure value of over 97%, despite it being trained with fewer samples than any of the classification systems we have encountered