A Review on Optimizing Radial Basis Function Neural Network using Nature Inspired Algorithm

Radial Basis Function (RBF) is a type of feed forward neural network .This function can be applied to interpolation, chaotic time-series modeling, control engineering, image restoration, data fusion etc. In RBF network, parameters of basis functions (such as width, the position and number of centers) in the nonlinear hidden layer have great influence on the performance of the network. Common RBF training algorithms cannot possibly find the global optima of nonlinear parameters in the hidden layer, and often have too many hidden units to reach certain approximation abilities, which will lead to too large a scale for the network and decline of generalization ability. Also, RBF neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Secondly, the Swarm Intelligence Algorithms are (Meta-Heuristic) development Algorithms, which attracted much attention and appeared its ability in the last ten years within many applications such as data mining, scheduling, improve the performance of artificial neural networks (ANN) and classification. So, in this paper the work of Artificial Bee Colony (ABC), Genetic algorithm(GA), Particle swarm optimization(PSO) and Bat algorithm(BA) have been reviewed, which optimized the RBF neural network in their own terms

Hacia la clasificación de fonocardiogramas utilizando descriptores caóticos y estadísticos

En este trabajo, tres descriptores: la dimensión de correlación, el exponente de Lyapunov y la entropía aproximada, son calculados a partir de series de tiempo procedentes de mediciones de fonocardiogramas, con el propósito de entrenar una red neuronal auto-organizada para clasificar afecciones cardíacas. El uso de la red neuronal, en conjunto con descriptores caóticos y estadísticos, muestran un buen desempeño y capacidad para lograr una segmentación entre clases.Palabra(s) Clave(s): análisis de series de tiempo, entropía aproximada,fonocardiograma, reconstrucción del atractor, red neuronal auto-organizada, teoría de caos

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results