The capability of generalization is the most desirable property of a learning system. It is well known that to achieve good generalization, the complexity of the system should match the intrinsic complexity of the problem to be learned. In this work, introduction of fractal connection structure in nonlinear learning systems like multilayer perceptrons as a means of improving its generalization capability in classification problems has been investigated via simulation on sonar data set in underwater target classification problem. It has been found that fractally connected net has better generalization capability compared to the fully connected net and a randomly connected net of same average connectivity for proper choice of fractal dimension which controlls the average connectivity of the net.

In this paper we examine the architectural and operational design issues of a practical network management system using the Simple Network Management Protocol (SNMP) in the context of a large-scale OSI-based campus-network TAINS. Various design aspects are examined and the importance of time-management is elicited. In the proposed design, intelligent, time-synchronised agents are deployed to collect information about the network segments to which they are attached. The manager talks to the agents and gathers relevant network information. This information is used by the expert network manager, in conjunction with a network knowledge base (NKB) and a management information knowledge base (MIKB) , to reconstruct the overall network-traffic characteristic, to evaluate the status of the network and to take/suggest some action. This model is particularly useful in networks where some global control, monitoring and management is desired and installing agents on all elements, connected to the network, is impossible. The use of time labels and narrow time windows enables the manager to obtain a reasonably accurate picture of the network status. The introduction of time-labelled composite objects in the Management Information Base (MIB) provides a means of reducing the load of management-related traffic on the network. The MIKB containing a logical description of the behaviour of the managed objects defined in the MIB, drives the expert system and provides the knowledge of general nature that a human expert has about networks. The proposed MIKB concept provides a very convenient schema for building the knowledge base in an expert network management system. Further since the MIKB is MIB-specific, it can be used in network management systems for managing similar MIB's.

Feature subset selection is an important preprocessing task for pattern recognition, machine learning or data mining applications. A Genetic Algorithm (GA) with a fuzzy fitness function has been proposed here for finding out the optimal subset of features from a large set of features. Genetic algorithms are robust but time consuming, specially GA with neural classifiers takes a long time for reasonable solution. To reduce the time, a fuzzy measure for evaluation of the quality of a feature subset is used here as the fitness function instead of classifier error rate. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function. Simulation over two data sets shows that the proposed algorithm is efficient for selection of near optimal solution in practical problems specially in case of large feature set problems.

Feature subset selection basically depends on the design of a criterion function to measure the effectiveness of a particular feature or a feature subset and the selection of a search strategy to find out the best feature subset. Lots of techniques have been developed so far which are mainly categorized into classifier independent filter approaches and classifier dependant wrapper approaches. Wrapper approaches produce good results but are computationally unattractive specially when nonlinear neural classifiers with complex learning algorithms are used. The present work proposes a hybrid two step approach for finding out the best feature subset from a large feature set in which a fuzzy set theoretic measure for assessing the goodness of a feature is used in conjunction with a multilayer perceptron (MLP) or fractal neural network (FNN) classifier to take advantage of both the approaches. Though the process does not guarantee absolute optimality, the selected feature subset produces near optimal results for practical purposes. The process is less time consuming and computationally light compared to any neural network classifier based sequential feature subset selection technique. The proposed algorithm has been simulated with two different data sets to justify its effectiveness.

Feature selection is an integral part of any pattern recognition system. Removal of redundant features improves the efficiency of a classifier as well as cut down the cost of future feature extraction. Recently neural network classifiers have become extremely popular compared to their counterparts from statistical theory. Some works on the use of artificial neural network as a feature selector have already been reported. In this work a simple feature selection algorithm has been proposed in which a fractal neural network, a modified version of multilayer perceptron, has been used as a feature selector. Experiments have been done with IRIS and SONAR data set by simulation. Results suggest that the algorithm with the fractal network architecture works well for removal of redundant informations as tested by classification rate. The fractal neural network takes lesser training time than the conventional multilayer perceptron for its lower connectivity while its performance is comparable to the multilayer perceptron. The ease of hardware implementation is also an attractive point in designing feature selector with fractal neural network.