Neural Network World; International Journal on Neural and Mass - Parallel Computing and Information Systems

Adaptive-Network-Based Fuzzy Inference System Models for Narrow Aperture Dimension Calculation of Optimum Gain Pyramidal Horns

A method based on the adaptive-network-based fuzzy inference system (ANFIS) is presented for computing the narrow aperture dimension of the pyramidal horn. Eight optimization algorithms, least-squares, hybrid learning, Nelder-Mead, genetic, differential evolution, particle swarm, simulated annealing, and clonal selection, are used to optimally determine the design parameters of the ANFIS. The narrow aperture dimension computed by using the ANFIS is used in the optimum gain pyramidal horn desi...

A Neural Network Approach for Global Optimization with Applications

We propose a neural network approach for global optimization with applications to nonlinear least square problems. The center idea is defined by the algorithm that is developed from neural network learning. By searching in the neighborhood of the target trajectory in the state space, the algorithm provides the best feasible solution to the optimization problem. The convergence analysis shows that the convergence of the algorithm to the desired solution is guaranteed. Our examples show that th...

Signal-to-Noise Ratios for Measuring Saliency of Features Extracted by Eigenvector Methods From Ecg Signals

Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. Features are used to represent patterns with minimal loss of important information. The feature vector, which is comprised of the set of all features used for describing a pattern, is a reduced-dimensional representation of that pattern. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification....

Wave Probabilisties and Quantum Entanglement

The paper continues with the theory of wave probabilistic models and uses the inclusion-exclusion rule to describe quantum entanglement as a wave probabilities resonance principle. The achieved results are mathematically described and an illustrative example is shown to demonstrate the possible applications of the presented theory.

Hierarchical Decomposition of Multiclass Problems

Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary sub-problems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorit...