This chapter includes contributions to the theory of on-line training of artificial neural networks (ANN), considering the multilayer perceptrons (MLP) topology. By on-line training, we mean that the learning process is conducted while the signal processing is being executed by the system, i.e., the neural network continuously adjusts its free parameters from the variations in the incident sign...

The Neural Networks are best at identifying patterns or trends in data and they are well suited for predicting or forecasting. Hence neural networks are extensively applied to biomedical systems. An analysis is carried out to motivate neural network applications in medical diagnosis. A special note is made on neural network effort on cancer diagnosis. This paper focuses on the importance of app...

In this paper an extension of SANE that simultaneously evolves the weights and architecture of an MLP neural network is presented. The symbiotic adaptive neuroevolution (SANE) system coevolves a population of neurons that cooperate to form a functioning neural network. Evolutionary Strategies (ES) is applied to evolve the network weights. In order to increase the evolving system performance and...

This paper investigates the application of artificial neural network structures to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such us inter symbol interference (ISI) and nonlinearities can be overcome by equalizers employing neural networks. We present a structure of a nonlinear equalizer with decision feedback using multilayer perceptron (MLP ...

A globally convergent homotopy method is deened that is capable of sequentially producing large numbers of stationary points of the multi-layer perceptron mean-squared error surface. Using this algorithm large subsets of the stationary points of two test problems are found. It is shown empirically that the MLP neural network appears to have an extreme ratio of saddle points compared to local mi...

In this contribution, human face as biometric [1] is considered. Original method of feature extraction from image data is introduced using MLP (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by MLP and...

Gaussian Mixture Model (GMM) and Multi Layer Perceptron (MLP) based acoustic models are compared on a French large vocabulary continuous speech recognition (LVCSR) task. In addition to optimizing the output layer size of the MLP, the effect of the deep neural network structure is also investigated. Moreover, using different linear transformations (time derivatives, LDA, CMLLR) on conventional M...

nowadays, software cost estimation (sce) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. in this paper, we present a new hybrid model of multi-layer perceptron (mlp) artificial neural network (ann) and ant colony optimization (aco) algorithm for high accuracy in sce called multilayer perceptron ant colony optimi...

Wind speed high-precision prediction is one of the most important technical aspects to protect the safety of wind power utilization. In this study, two new hybrid methods [FEEMD-MEA-MLP/FEEMD-GA-MLP] are proposed for the wind speed accurate multi-step predictions by combining FEEMD (Fast Ensemble Empirical Mode Decomposition), MEA (Mind Evolutionary Algorithm), GA (Genetic Algorithm) and MLP (M...

This paper compares three mathematical models for surgical decisions on head injury patients. A logistic regression and two neural network models were developed using a large clinical database. Using randomly selected 9480 cases as the training group and another 3160 cases as the validation group. We evaluated the performance of a logistic regression model, a multi-layer perceptron (MLP) neural...