Detection of Alzheimer's disease on brain Magnetic Resonance Imaging (MRI) is a highly sought goal in the Neurosciences. We used four di erent models of Arti cial Neural Networks (ANN): Backpropagation (BP), Radial Basis Networks (RBF), Learning Vector Quantization Networks (LVQ) and Probabilistic Neural Networks (PNN) to perform classi cation of patients of mild Alzheimer's disease vs. control...

Abstract The bonding quality of composite plate material is detected by ultrasonic waves. Taking ultrasonic detection signal as the research object, the theory of detection method pulse reflection echo method is comprehensively analyzed. Surveying the information carried by the echo signal of detection ultrasonic waves, the signal energy, signal duration and the product of singular wave peak va...

In this paper, an ISS-modular adaptive neural tracking control approach is presented for a class of completely non-affine pure-feedback systems combining backstepping with input-to-state stability (ISS) and small gain theorem. From the second step of backstepping, correlative interconnection terms are defined and introduced in implicit functions. Since the introduction of the correlative interc...

In this paper, Multi-Layer Perceptron and RadialBasis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3and 6-h lead time prediction and the only reliable one for 9-h lead time forecas...

three artificial neural networks (ann) models; general regression neural network (grnn), redial basis function (rbf) and three layer multiple perceptron network were carried out to evaluate the prediction of the apparent metabolizable energy (ame) of wheat and corn from its chemical composition in broiler. input variables included: gross energy (ge), crude protein (cp), crude fiber (cf), ether ...

A modelling of robot manipulator dynamics by means of a neural architecture is presented. Such model is applicable to generate a decoupling and linearising feedback in the control system of the robot. In a structured model approach, a RBF-like neural network is used to represent and adapt all model parameters with their dependences on the joint positions. The neural network is hierarchically or...

Abstract— In a reverberant environment, the performance of acoustic event recognition system can be bolstered by choosing appropriate feature descriptors and classifier techniques. Neural networks are by far providing stunning classification results when compared to other classifiers. This paper analyses two different neural networks and their precision when they both stumble upon same targets ...

This paper presents a comparative study of the hybrid models, neural networks and nonparametric regression models in time series forecasting. The components of these hybrid models are consisting of the nonparametric regression and artificial neural networks models. Smoothing spline, regression spline and additive regression models are considered as the nonparametric regression components. Furth...

In view of disasters caused by rock burst becoming more and more serious in coal mine production, three models are established for evaluation and prediction the rock burst risk based on artificial neural network. First, ten indicators are determined which have a larger influence on rock burst. Then two back propagation network models are trained using the original data and the processed data re...

Dimension-reducing feature extraction neural network techniques which also preserve neighbourhood relationships in data have traditionally been the exclusive domain of Kohonen self organising maps. Recently, we introduced a novel dimension-reducing feature extraction process, which is also topographic, based upon a Radial Basis Function architecture. It has been observed that the gener-alisatio...